Advanced Sensor Systems for Drought Stress Detection in Tomatoes: A Comprehensive Guide for Researchers and Scientists

Abstract

This article provides a comprehensive analysis of the latest sensor-based technologies for detecting and managing drought stress in tomato cultivation. Aimed at researchers, scientists, and agricultural technologists, it explores the foundational physiological responses of tomatoes to water scarcity, details cutting-edge methodological applications including wearable sensors, hyperspectral imaging, and IoT-enabled deep learning systems. The content further addresses critical challenges in sensor deployment and data optimization, and presents rigorous validation frameworks and comparative analyses of tomato genotypes. By synthesizing recent advances in high-throughput phenotyping and precision agriculture, this resource aims to bridge the gap between sensor-derived data and actionable biological insights for improved crop resilience and water-use efficiency.

Understanding Tomato Drought Physiology: The Basis for Sensor-Based Detection

Key Physiological Responses to Drought Stress in Tomatoes

Drought stress is a major abiotic factor that destructively limits the growth, development, and yield of tomato (Solanum lycopersicum L.), posing a significant threat to global food security under increasing climate change pressures [1]. For researchers aiming to implement sensor systems for drought stress detection, a thorough understanding of the key physiological responses is essential. This document details the primary physiological parameters altered by water deficit, provides standardized protocols for their quantification, and visualizes the underlying signaling pathways to support the development of robust phenotyping and diagnostic tools.

Core Physiological Responses and Quantitative Data

Drought stress triggers a cascade of physiological changes in tomato plants. The tables below summarize the key morphological, physiological, and biochemical parameters that serve as vital indicators for stress severity and cultivar tolerance, providing a quantitative basis for sensor calibration and data interpretation.

Table 1: Morphological and Growth Responses of Tomato to Drought Stress

Trait	Change Under Drought	Quantitative Example	Significance for Sensing
Shoot Length	Decrease	21-37% reduction in susceptible cultivar [1]	Can be monitored via automated image-based phenotyping [2].
Root Length	Variable	May increase in tolerant genotypes as a compensatory mechanism [3]	Root architecture is harder to sense remotely; may require rhizotron systems.
Fresh Weight (FW)	Decrease	~50% reduction in susceptible cultivar [1]	Indicator of tissue water status and overall biomass.
Dry Weight (DW)	Decrease	Up to 29% reduction [1]	Reflects long-term impact on biomass accumulation and productivity.
Fruit Yield	Decrease	44% average reduction across landraces [4]	Ultimate agronomic indicator of stress impact.

Table 2: Physiological and Biochemical Responses of Tomato to Drought Stress

Trait	Change Under Drought	Quantitative Example	Significance for Sensing
Relative Water Content (RWC)	Decrease	20-30% reduction in susceptible cultivar [1]	Direct measure of leaf water status; a key validation metric for sensors.
Chlorophyll Content	Decrease	7-23% reduction [1]	Can be estimated via spectral sensors or chlorophyll fluorescence.
Maximum Quantum Yield of PSII (Fv/Fm)	Decrease	8.2% reduction after 14 days of stress [4]	Sensitive indicator of photosynthetic apparatus health; measurable by PEA fluorometer.
Lipid Peroxidation (TBARs)	Increase	14% increase in susceptible cultivar [1]	Marker of oxidative damage to cell membranes.
Antioxidant Enzyme Activity (SOD, CAT, POX)	Increase in tolerant cultivars	50-67% increase in CAT activity in resistant cultivar [1]	Biochemical indicator of the plant's active defense mechanism.
Stomatal Conductance (gsw)	Decrease	Strongly correlated with CO2 assimilation rate (An) [5]	Key early response; can be inferred from thermal imaging and Tdiff.

Experimental Protocols for Inducing and Quantifying Drought Stress

Protocol: PEG-Induced Osmotic Stress for Early Seedling Stage

This method uses polyethylene glycol (PEG) to simulate controlled drought stress in a laboratory setting, ideal for high-throughput screening of genotypes [1] [3].

• Key Applications: Screening germination performance and seedling vigor under standardized osmotic stress; identifying tolerant genotypes for breeding programs.
• Materials:
  • Tomato seeds of genotypes to be tested.
  • Polyethylene Glycol 6000 (PEG 6000).
  • Petri dishes, sterile blotting paper, growth chambers.
  • Hoagland nutrient solution.
• Procedure:
  • Surface Sterilization: Sterilize seeds with 70% ethanol for 1 minute, followed by 5% sodium hypochlorite for 5 minutes. Rinse thoroughly 3 times with sterile distilled water [1].
  • PEG Solution Preparation: Prepare aqueous solutions of PEG 6000. Common concentrations for screening are 3% (-0.18 MPa), 6% (-0.36 MPa), and 10% for severe stress [1] [3].
  • Germination Setup: Place sterilized seeds on Petri dishes containing blotting paper moistened with the PEG solutions or control (distilled water). Use 30 seeds per replicate for statistical robustness [1].
  • Growth Conditions: Incubate seeds under controlled conditions (e.g., 25±2°C with a 16/8 hour light/dark photoperiod).
  • Data Collection: After 7 days, record:
    • Germination Percentage: (Number of germinated seeds / Total seeds) × 100 [1].
    • Seedling Vigor: Measure root and shoot length of seedlings.
    • Biomass: Record fresh and dry weights of seedlings.

Protocol: Regulated Deficit Irrigation for Pot or Field Studies

This protocol applies water stress to mature plants, mimicking field conditions and allowing for yield-related measurements [4].

• Key Applications: Evaluating the impact of sustained water deficit on physiological performance, fruit development, and final yield; validating laboratory findings in a more realistic environment.
• Materials:
  • Tomato seedlings.
  • Pots with soil or field plot access.
  • Soil moisture sensors (e.g., Caipos Wave station or similar).
  • Drip irrigation system.
• Procedure:
  • Plant Establishment: Transplant seedlings and maintain at optimal irrigation (100% field capacity) until establishment.
  • Calculate Irrigation Requirement (IR):
    • IR = [10 × Root Zone Depth (m) × Bulk Density (g/cm³) × (Field Capacity - Pre-irrigation Soil Moisture)] × K [4].
    • K is a reduction coefficient accounting for the irrigated area (e.g., 0.66 for 66% coverage).
  • Stress Induction: 20 days after transplanting, initiate two regimes:
    • Control: 100% of calculated IR.
    • Drought Stress: 50% of calculated IR [4].
  • Monitoring: Adjust irrigation every 2 days based on soil moisture sensor readings to maintain the target stress level.
  • Data Collection:
    • Physiological Traits: Periodically measure RWC, Chlorophyll Fluorescence (Fv/Fm, Fv/Fo), and stomatal conductance.
    • Morphological Traits: Record flower and fruit number, and final yield per plant.
    • Fruit Quality: Assess fruit firmness and color indexes at maturity [4].

Protocol: Measurement of Key Physiological Parameters

• Relative Water Content (RWC):

  • Collect fresh leaf discs and immediately record their Fresh Weight (FW).
  • Hydrate discs in distilled water for 4 hours in darkness, then blot dry and record Turgid Weight (TW).
  • Dry discs in an oven at 80°C for 24 hours and record Dry Weight (DW).
  • Calculate RWC as: RWC (%) = [(FW - DW) / (TW - DW)] × 100 [1].

• Chlorophyll a Fluorescence:

  • Dark-adapt a fully expanded leaf for at least 20 minutes.
  • Use a portable fluorometer (e.g., Plant Efficiency Analyzer, PEA) to apply a saturating light pulse and measure:
    • Fâ‚€: Minimal fluorescence.
    • Fm: Maximal fluorescence.
  • Calculate key parameters:
    • Fv/Fm = (Fm - Fâ‚€) / Fm (Maximum quantum efficiency of PSII) [4].
    • Fv/Fâ‚€ (Maximum primary yield of photochemistry of PSII) [4].

Signaling Pathways and Experimental Workflows

Oxidative Stress and Defense Signaling Pathway

This diagram visualizes the core biochemical pathway activated in tomato leaves under drought stress, leading to oxidative damage and the induction of defense mechanisms.

Title: Drought-Induced Oxidative Stress and Defense in Tomato

Integrated Phenotyping Workflow for Drought Response

This diagram outlines a comprehensive experimental workflow, from stress imposition to multi-level data collection, which is fundamental for developing sensor-based prediction models.

Title: Integrated Workflow for Drought Phenotyping

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Reagents and Kits for Drought Stress Research

Item	Function/Application	Example Use in Protocol
Polyethylene Glycol 6000 (PEG 6000)	Osmoticum to simulate drought stress by lowering water potential in growth media.	Preparation of 3%, 6%, and 10% solutions for in vitro screening of germination and seedling growth [1] [3].
Hoagland Nutrient Solution	Standardized nutrient solution for plant growth under controlled conditions.	Providing essential nutrients to control and stressed plants in hydroponic or perlite-based systems [1].
Portable Chlorophyll Fluorometer (e.g., PEA)	Non-destructive measurement of PSII efficiency and photosynthetic performance.	Measuring Fv/Fm and Fv/Fo parameters in dark-adapted leaves to assess stress impact on photosynthesis [4].
Leaf Porometer	Measures stomatal conductance (gsw) to quantify plant water status and transpiration.	Direct in-situ measurement of stomatal closure as an early response to water deficit [5].
Spectrophotometer & Assay Kits	Quantitative analysis of biochemical markers (e.g., Hâ‚‚Oâ‚‚, TBARs, Antioxidant Enzyme Activity).	Measuring levels of lipid peroxidation (TBARs) and activities of SOD, CAT, and POX in leaf tissue extracts [1].
Soil Moisture Sensors	Monitor volumetric water content or soil water potential in real-time.	Precisely controlling and maintaining the level of water deficit (e.g., 40% FC) in pot or field experiments [4].
CART Model	A classification and regression tree algorithm for data-driven decision making.	Classifying drought status (Low, Medium, High) using environmental variables (Tair, VPD, Tdiff) [5].
Lin28-IN-1	Lin28-IN-1|Lin28 Protein Inhibitor|For Research Use	Lin28-IN-1 is a chemical inhibitor targeting the Lin28/let-7 pathway. This product is for Research Use Only (RUO) and not for human or veterinary use.
Pkm2-IN-4	Pkm2-IN-4, MF:C15H17BrClNO3Se, MW:453.6 g/mol	Chemical Reagent

The process of crop domestication has fundamentally shaped modern agriculture, often favoring high-yielding traits at the expense of stress resilience. This tradeoff between productivity and survival presents a significant challenge for sustainable crop production, particularly in the face of increasing drought events due to climate change. Tomato (Solanum lycopersicum), as a model crop system, provides critical insights into these domestication trade-offs and potential strategies to overcome them.

Recent research reveals that cultivated tomato varieties have undergone a narrowing of their fundamental nutritional niches (FNNs) during domestication, mirroring patterns observed in other domesticated systems [6]. This specialization enhances productivity under optimal conditions but increases vulnerability to environmental stressors such as drought. Understanding these trade-offs provides the foundation for developing innovative approaches to enhance drought resilience while maintaining productivity.

This application note explores the implementation of sensor systems for drought stress detection in tomato research, providing detailed protocols for investigating domestication trade-offs and enhancing drought resilience through grafting, organic amendments, and advanced phenotyping technologies.

Theoretical Framework: Domestication Trade-offs in Tomato

The Nutritional Niche Trade-off

Research across farming systems, including attine ants and human agriculture, reveals a consistent domestication trade-off: higher productivity is achieved at the cost of increased vulnerability outside specialized cultivation conditions [6]. In tomato, this manifests as narrowed fundamental nutritional niches (FNNs) in domesticated varieties compared to their wild relatives.

Key findings from nutritional geometry studies:

• Cultivar FNNs narrowed during ~60 million years of naturally selected domestication
• Fully domesticated cultivars maximize growth within specific protein:carbohydrate ratios (typically 8-15 g/L protein at low carbohydrate concentrations)
• Wild relatives and landraces exhibit broader FNNs, enabling resilience across diverse environmental conditions
• Leafcutter ant cultivars (Leucoagaricus gongylophorus) show similar specialized FNNs without increased growth rates compared to basal attine cultivars [6]

Physiological Consequences of Domestication

The domestication-driven specialization affects multiple physiological processes in tomato:

Photosynthetic Performance: Mediterranean landraces demonstrate superior photosystem resilience under intermittent drought conditions compared to commercial cultivars [7]. Specific landraces ('260') maintained stable OJIP profiles (chlorophyll a fluorescence parameters) under sustained water deficit, while commercial varieties showed significant reductions in performance index (PIABS) and quantum yield (Ï•P0) [7].

Recovery Patterns: A three-stage resilience model emerges under drought stress: (i) reversible photoprotective adjustment to short severe drought; (ii) cumulative photochemical damage under sustained deficit; and (iii) genotype-dependent recovery capacity [7].

Table 1: Key Drought Resilience Traits in Tomato Landraces vs. Commercial Cultivars

Trait	Mediterranean Landraces	Commercial Cultivars	Measurement Method
Fundamental Nutritional Niche	Broad FNNs	Narrowed FNNs	In vitro growth across nutritional landscapes [6]
PSII Resilience (PIABS)	Stable under sustained drought (e.g., '260')	Reduced by 18-50% under WS2	Chlorophyll a fluorescence (OJIP test) [7]
Stomatal Regulation	Reversible photoprotection	Often irreversible damage	Gas exchange, Ψstem measurements [7]
Recovery Capacity	Complete photosynthetic recovery	Partial recovery (e.g., '264' recovered only 50%)	Post-rehydration OJIP parameters [7]

Sensor Systems for Drought Stress Detection

In Vivo Sensing Technologies

Advanced sensor technologies enable real-time monitoring of plant physiological status, providing critical data for investigating domestication trade-offs and plant responses to drought stress.

Bioristor (OECT-based sensor):

• Operating Principle: Organic ElectroChemical Transistor integrated within the plant stem continuously monitors changes in ion concentration in the xylem sap [8] [9]
• Detection Capability: Efficiently detects drought stress immediately after defense response initiation by monitoring dissolved ions transported through the transpiration stream [8]
• Field Performance: Enables 36% or more reduction in water supply while maintaining plant health through real-time adjustment of irrigation [9]
• Application: Successfully operated for 54-62 days from flowering to harvest in field conditions [9]

PlantRing Wearable Sensor System:

• Technology: Nano-flexible sensing system using bio-sourced carbonized silk georgette as strain-sensing material [10]
• Performance Specifications: Detection limit of 0.03%-0.17% strain, tensile strain up to 100%, durability for season-long use [10]
• Applications: Monitors plant growth and water status via organ circumference dynamics; enables large-scale quantification of stomatal sensitivity to soil drought [10]
• Research Utility: Facilitates selection of drought-tolerant germplasm and reveals genotype-specific hydraulic mechanisms [10]

Integration with High-Throughput Phenotyping

The combination of in vivo sensors with high-throughput phenotyping platforms provides complementary data streams for comprehensive drought response analysis [8]. This integrated approach links real-time physiological status (from bioristor) with whole-plant performance metrics, enabling researchers to connect molecular and cellular events with organism-level outcomes of domestication trade-offs.

Experimental Protocols for Investigating Domestication Trade-offs

Protocol 1: Assessing Nutritional Niches and Drought Resilience

Objective: Quantify fundamental nutritional niches and drought resilience traits in tomato genotypes representing different domestication stages.

Materials:

• Tomato genotypes: Wild relatives, landraces, and commercial cultivars
• PlantRing sensor systems [10] or equivalent stem diameter monitoring
• Chlorophyll fluorescence imaging system
• Materials for in vitro culture: PDA medium, nutritional components

Procedure:

• Genotype Selection and Cultivation:

  • Select at least 3 genotypes from each domestication category (wild, landrace, modern)
  • Grow 15 plants per genotype under controlled conditions (25-30°C, 60-80% RH)
  • Apply standard cultivation practices until 5th true leaf stage

• Sensor Integration:

  • Install PlantRing sensors on main stems of 10 plants per genotype
  • Calibrate sensors according to manufacturer specifications
  • Begin continuous monitoring of stem circumference dynamics

• Drought Stress Application:

  • Divide plants into control and drought stress groups (n=5 per treatment)
  • Withhold irrigation for drought group while maintaining control at 70% field capacity
  • Monitor soil moisture daily using moisture meters

• Physiological Measurements:

  • Day 0, 3, 7, 10 of drought: Measure chlorophyll fluorescence (Fv/Fm, PIABS), stomatal conductance, and stem water potential (Ψstem)
  • Continuous: Record sensor outputs (PlantRing circumference, bioristor R index if available)
  • Day 10: Collect leaf samples for transcriptomic/proteomic analysis

• Recovery Assessment:

  • Rewater drought-stressed plants and monitor recovery for 7 days
  • Measure photosynthetic parameters at 24h, 72h, and 168h post-rewatering

• Data Analysis:

  • Calculate stress integral (SI) from Ψstem measurements
  • Correlate sensor outputs with physiological measurements
  • Compare recovery capacity across domestication categories

Protocol 2: Grafting for Enhanced Drought Resilience

Objective: Evaluate the potential of grafting to mitigate domestication trade-offs by combining resilient rootstocks with productive scions.

Materials:

• Drought-tolerant rootstocks (e.g., Shivam, Arka Samrat) [11]
• Drought-susceptible scions (e.g., Arka Rakshak, Arka Apeksha) [11]
• Grafting supplies: razor blades, clips, transparent plastic covers
• RNA/DNA extraction kits for molecular analysis

Procedure:

• Plant Material Preparation:

  • Surface-sterilize seeds with 1% sodium hypochlorite
  • Sow in germination trays with 1:1:1 soil:sand:vermicompost mixture
  • Maintain in greenhouse at 25-30°C with 12h/12h light/dark cycle

• Grafting (25 days after sowing):

  • Perform cleft grafting in early morning conditions
  • Create 6 combinations with tolerant rootstocks and susceptible scions
  • Include homografted controls for comparison
  • Cover grafts with transparent plastic to maintain 90-95% humidity

• Acclimation and Transplanting:

  • Maintain high humidity for 14 days until graft unions heal
  • Transplant uniform healthy seedlings to grow bags (2 plants/bag)
  • Continue standard cultivation for 40 days post-grafting

• Drought Treatment and Sampling:

  • Divide plants into control and drought stress groups
  • Withhold irrigation for 10 days for drought treatment
  • Monitor soil moisture daily
  • Collect leaf samples after 10 days for molecular analyses

• Molecular Analysis:

  • Extract RNA/DNA from stored leaf samples (-80°C)
  • Analyze expression of stress-responsive genes (DREB, WRKY, PIPs, SOD, CAT, APX, HSPs, LOX)
  • Perform proteomic analysis to identify cellular processes and stress response pathways

Protocol 3: Organic Amendments for Soil Resilience

Objective: Assess the efficacy of organic amendments in mitigating drought stress effects across tomato genotypes with different domestication backgrounds.

Materials:

• Tomato landraces ('TR40430', 'Areti') and commercial control ('Moneymaker') [12]
• Organic amendments: biochar (from olive cake pyrolysis), vermicompost (from cattle manure)
• Soil analysis kits for microbial activity and chemical properties

Procedure:

• Experimental Setup:

  • Apply biochar and vermicompost at 1 kg m⁻² to field plots before planting
  • Incorporate amendments to 15 cm depth
  • Include control plots without amendments

• Irrigation Treatments:

  • Implement three irrigation regimes:
    • Full irrigation (Ir100): replenish to field capacity
    • Deficit irrigation (Ir70): 70% of Ir100 volume
    • Severe deficit (Ir40): 40% of Ir100 volume
  • Use drip irrigation system for precise water application

• Data Collection:

  • Vegetative Growth: Plant height, fresh/dry weight of vegetative parts, leaf thickness
  • Yield Components: Total yield, fruit quality parameters (TSS, TA, firmness)
  • Physiological Parameters: Chlorophyll index, stomatal conductance, leaf water potential
  • Soil Metrics: Microbial activity, water retention characteristics

• Statistical Analysis:

  • Use AMMI (Additive Main Effects and Multiplicative Interaction) model to evaluate genotype × environment interactions
  • Calculate water use efficiency (WUE) for each treatment combination
  • Analyze treatment effects on soil microbial functioning

Research Reagent Solutions and Materials

Table 2: Essential Research Reagents and Materials for Domestication Trade-off Studies

Category	Specific Product/Technology	Application/Function	Example Use Case
Sensor Systems	Bioristor (OECT-based)	In vivo monitoring of xylem sap ion concentration	Real-time drought detection in field tomatoes [8] [9]
	PlantRing	Wearable sensor for stem circumference dynamics	High-throughput phenotyping of stomatal sensitivity [10]
Molecular Analysis	RNA/DNA extraction kits	Nucleic acid isolation for transcriptomic studies	Gene expression analysis of stress-responsive genes [11] [13]
	qRT-PCR reagents	Quantitative gene expression validation	Verification of meta-DEG expression patterns [13]
Soil Amendments	Biochar (olive cake source)	Soil amendment for water retention	Enhancing soil resilience under deficit irrigation [12]
	Vermicompost (cattle manure)	Organic fertilizer and soil conditioner	Improving microbial activity under drought stress [12]
Physiological Measurement	Chlorophyll fluorometer	OJIP testing for PSII functionality	Assessing photosystem resilience under drought [7]
	Soil moisture meters	Monitoring soil water content	Quantifying drought stress intensity and progression [11] [12]

Visualization of Experimental Approaches

Integrated Workflow for Domestication Trade-off Analysis

Molecular Mechanisms of Drought Resilience in Tomato

The investigation of trade-offs between productivity and survival in tomato domestication provides crucial insights for developing climate-resilient crops. Integration of advanced sensor technologies with traditional physiological and molecular approaches enables comprehensive analysis of drought resilience mechanisms across different domestication stages.

Future research directions should focus on:

• Multi-omics Integration: Combining transcriptomic, proteomic, and metabolomic data with real-time sensor outputs to build predictive models of drought resilience
• Sensor Technology Advancement: Developing more affordable, scalable sensor systems for high-throughput phenotyping in breeding programs
• Precision Management: Implementing sensor-informed irrigation management to optimize water use while maintaining productivity
• Gene Discovery: Leveraging identified meta-DEGs and resilience traits for marker-assisted selection and genetic engineering

The protocols and methodologies outlined in this application note provide researchers with comprehensive tools for investigating domestication trade-offs and developing strategies to enhance drought resilience in tomato and other crops.

Stomatal Conductance and Water-Use Efficiency as Critical Phenotyping Targets

Application Note: Integrating Sensor Systems for Drought Phenotyping in Tomatoes

This application note outlines established protocols for utilizing sensor-based systems to phenotype stomatal conductance (gsw) and water-use efficiency (WUE) in tomato, critical traits for breeding drought-resilient cultivars. The integration of real-time soil monitoring, plant-based sensors, and modeling provides a multifaceted understanding of plant responses to water deficit, enabling early stress detection and precise irrigation management.

Table 1: Key Quantitative Findings from Tomato Drought Phenotyping Studies

Phenotyping Approach	Key Measured Parameters	Quantitative Findings	Citation
Soil Moisture-Dependent Physiology	Volumetric Water Content (VWC), VOCs, Water Use Efficiency (WUE)	Optimal irrigation VWC: 15-25%; Critical wilting at 7.5% VWC; VOC (caryophyllene, β-phellandrene) doubled as VWC dropped from 25% to 15%; Achieved IWUE >60 kg/m³.	[14]
Modified Ball-Berry Model	Stomatal Conductance (gsw), Photosynthetic Rate (Pn), WUE	Model incorporating mean soil water potential (Ψs) showed greater predictability for gs and WUE under deficit irrigation (DI) and partial root-zone irrigation (PRI).	[15]
CART Model for Drought Classification	Air Temperature (Tair), Vapor Pressure Deficit (VPD), Leaf-Air Temp Difference (Tdiff)	The CART model classified drought status (Low, Medium, High) with high predictive accuracy (sensitivity, specificity) across three tomato genotypes.	[5]
Wearable Hydrogen Peroxide Sensor	Hydrogen Peroxide (Hâ‚‚Oâ‚‚)	Electrochemical patch detected Hâ‚‚Oâ‚‚, a key distress signal, in infected plants within 1 minute; reusable up to 9 times.	[16]
Hyperspectral Imaging & Deep Learning	Machine Learning Vegetation Index (MLVI), Hyperspectral Vegetation Stress Index (H_VSI)	A CNN model using novel indices achieved 83.40% accuracy in classifying six levels of crop stress severity, detecting stress 10-15 days earlier than conventional indices.	[17]

Experimental Protocols

Protocol 1: Multilateral Soil Moisture-Dependent Phenotyping

This protocol identifies optimal irrigation ranges by correlating real-time soil moisture with morphological, physiological, and biochemical plant responses [14].

1.1 Sensor Calibration and Placement

• Materials: Capacitance soil moisture sensors (e.g., 5TE sensors), data logger.
• Procedure:
  • For three-layered soils (e.g., siliceous sand mulch, manure, clay), position sensors at multiple depths (e.g., within the manure layer at 7.5 cm) to capture root-zone dynamics.
  • Calibrate sensors to measure Volumetric Water Content (VWC) dynamically.
  • Implement a drip irrigation system for precise water application.

1.2 Plant Response Monitoring

• Morphological & Physiological: Track visible wilting, leaf gas exchange (photosynthetic rate, transpiration), and stomatal conductance. WUE is calculated as the ratio of photosynthetic rate to transpiration rate [14] [15].
• Biochemical: Sample leaves for primary metabolites (proline, GABA, malic acid, citric acid, fructose, glucose) via GC-MS when VWC drops to critical levels (e.g., 7.5%). Monitor volatile organic compounds (VOCs) like caryophyllene and β-phellandrene, which are early indicators of stress [14].

1.3 Data Integration

• Correlate time-series VWC data with all measured plant responses to define soil moisture thresholds that trigger stress, thereby establishing an optimal irrigation range (e.g., VWC of 15-25%) [14].

Protocol 2: Modeling Stomatal Conductance and WUE Using a Modified Ball-Berry Model

This protocol details the use of a modified Ball-Berry model to predict gsw and WUE of tomato leaves under different irrigation regimes and CO2 environments [15].

2.1 Gas Exchange Measurements

• Materials: Portable gas exchange system with a leaf chamber.
• Procedure:
  • On intact leaves, measure net photosynthetic rate (Pn), atmospheric CO2 concentration (Ca), boundary layer conductance (gb), partial pressure of water vapor in the air (ea), and leaf temperature (T).
  • Conduct measurements under progressive soil drying or different irrigation strategies (Full Irrigation, Deficit Irrigation, Partial Root-zone Irrigation).

2.2 Model Parameterization and Calculation

• Core Equation: The modified Ball-Berry model is expressed as: gs = m * (Pn * hs / Cs) + g0, where m = mi * e^(-β * Ψs) [15].
• Input Calculations:
  • Compute relative humidity at the leaf surface (hs) using Equation 5 from [15].
  • Calculate CO2 concentration at the leaf surface (Cs) using Equation 3 from [15].
  • Determine the mean soil water potential (Ψs) in the root zone.
• Parameter Fitting: Use statistical software (e.g., PROC NLIN in SAS) to derive the parameters g0 (residual conductance) and mi (initial slope) by fitting the model to your measured gs data.
• WUE Calculation: After estimating gs, calculate leaf-level WUE as WUE = Pn / Tr, where transpiration (Tr) is derived from Equation 10 in [15].

Protocol 3: Classifying Drought Status Using a CART Model

This protocol employs a Classification and Regression Tree (CART) model to classify the drought status of tomatoes based on environmental variables [5].

3.1 Data Collection

• Materials: Infrared thermometer, climate station.
• Procedure:
  • Simultaneously measure leaf temperature (Tleaf) and air temperature (Tair). Calculate the leaf-air temperature difference (Tdiff = Tleaf - Tair).
  • Calculate Vapor Pressure Deficit (VPD) from air temperature and relative humidity.
  • Measure reference stomatal conductance (gsw) using a porometer to establish "ground truth" drought status (e.g., Well-Watered vs. Water-Stressed, or Low/Medium/High stress).

3.2 Model Development and Validation

• Software: Use statistical software with CART algorithm capabilities (e.g., R, Python scikit-learn).
• Procedure:
  • Use 70% of the dataset to train the CART model. The predictor variables are Tair, VPD, and Tdiff. The target variable is the drought status category based on gsw.
  • The algorithm will automatically split the data into nodes based on the most discriminatory variables (e.g., Tdiff < 1.5°C).
  • Validate the model's predictive performance (sensitivity, specificity, accuracy) using the remaining 30% of the data and datasets from different tomato genotypes.

Visualization of Workflows and Models

Diagram 1: Soil Moisture-Dependent Phenotyping Workflow

Title: Soil Moisture Phenotyping Workflow

Diagram 2: Structure of the Modified Ball-Berry Model

Title: Modified Ball-Berry Model Structure

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials and Reagents for Drought Phenotyping Experiments

Item	Function/Application	Example/Specification
Capacitance Soil Moisture Sensor	Real-time monitoring of volumetric water content (VWC) at root zone.	5TE sensor (70 MHz frequency) for dynamic VWC tracking [14].
Portable Gas Exchange System	Simultaneous measurement of leaf gas exchange parameters: photosynthetic rate (Pn), stomatal conductance (gsw), and transpiration rate (Tr).	Used to calculate instantaneous Water-Use Efficiency (WUE = Pn/Tr) and parameterize the Ball-Berry model [15].
Infrared Thermometer	Non-contact measurement of leaf temperature for calculating the leaf-air temperature difference (Tdiff), a key input for CART and CWSI models.	Essential for high-throughput field phenotyping [5].
Wearable Hydrogen Peroxide Patch	Early detection of biotic and abiotic stress by directly sensing Hâ‚‚Oâ‚‚, a key plant distress signal, on live leaves.	Electrochemical sensor with micro-needles; provides results in <1 minute [16].
Hyperspectral Imaging System	Captures detailed spectral reflectance data for early stress detection before visible symptoms appear. Can be UAV-mounted.	Used to develop sensitive vegetation indices (e.g., MLVI, H_VSI) for early stress detection [17].
Polyethylene Glycol (PEG) 6000	Used in controlled laboratory experiments to simulate drought stress by inducing osmotic stress in the growth medium.	Applied at concentrations of 0%, 3%, and 6% to screen genotypes for drought tolerance at early growth stages [3].
Cdk9-IN-24	CDK9 Inhibitor Cdk9-IN-24
Mmp-1-IN-1	Mmp-1-IN-1, MF:C14H17ClN2O3, MW:296.75 g/mol	Chemical Reagent

The study of plant adaptation to abiotic stresses, such as drought, requires an integrated understanding of both morphological and physiological dimensions. Morphology, the study of an organism's form and structure, and physiology, the study of its functions and processes, represent complementary biological perspectives that together determine a plant's resilience [18] [19]. In the context of drought stress in tomatoes (Solanum lycopersicum), these adaptations are particularly critical. While domesticated cultivars often prioritize yield and uniformity, wild genotypes frequently retain valuable stress resilience traits that have been inadvertently selected against during domestication [20]. This application note, framed within a broader thesis on implementing sensor systems for drought stress detection, delineates standardized protocols for characterizing these adaptations and provides a toolkit for their investigation, enabling researchers to systematically decode the complex drought response mechanisms in tomato.

Conceptual Framework: Distinguishing Morphology and Physiology

A clear operational distinction between morphological and physiological traits is fundamental for experimental design and data interpretation.

• Morphological Adaptations refer to structural changes in response to drought. These are often observable and static, providing a snapshot of the plant's structural investment to cope with stress [18]. In tomatoes, this includes alterations to the root system architecture (e.g., deeper rooting, increased root-to-shoot ratio) and leaf morphology (e.g., reduced leaf area, thicker cuticles) to minimize water loss and optimize water uptake [3].

• Physiological Adaptations encompass the dynamic functional and biochemical processes that maintain homeostasis under water deficit. These include the regulation of stomatal conductance to balance COâ‚‚ uptake with transpirational water loss, the synthesis of osmoprotectants for cellular osmotic adjustment, and the activation of antioxidant systems to mitigate oxidative damage [18] [21]. These processes are often immediate and reversible, reflecting the plant's real-time response to its environment.

The table below summarizes the core differences between these two adaptive strategies.

Table 1: Core Differences Between Morphological and Physiological Adaptations

Aspect	Morphological Adaptations	Physiological Adaptations
Nature & Focus	Structural, form-oriented [18]	Functional, process-oriented [18]
Temporal Scale	Long-term, developmental; relatively static [18]	Short-term, dynamic; rapid and reversible [21]
Key Examples in Tomato Drought Response	Increased root-to-shoot ratio, reduced leaf area [3]	Stomatal closure, osmotic adjustment, hormonal regulation [21]
Primary Investigation Methods	Imaging, microscopy, biomass measurement [18] [3]	Gas exchange analysis, metabolite profiling, molecular techniques [21] [13]

Experimental Data: Quantitative Comparisons of Adaptive Traits

The genetic bottleneck during tomato domestication has resulted in cultivated varieties possessing less than 5% of the genetic variation found in their wild relatives [20]. This erosion of diversity is particularly pronounced for complex traits like drought resilience. The following data, synthesized from recent studies, quantifies the differential performance between wild and domesticated genotypes.

Table 2: Morphological and Physiological Drought Response in Tomato Genotypes under PEG-Induced Stress (0% vs 6% PEG)

Genotype / Trait	Germination Percentage	Seedling Vigor Index	Root-to-Shoot Ratio	Physiological Status (Sensor Output)
NGRCO9569 (Wild)	Maintained at high level (>90%) [3]	High; minimal reduction [3]	Increased under stress [3]	Maintained near-normal ion transport [8]
Khumal 2 (Domesticated)	Maintained at 3% PEG; reduced at 6% [3]	Moderate; tolerant [3]	Increased under stress [3]	Moderate shift indicative of stress response [8]
Srijana (Domesticated, Susceptible)	Drastic reduction to 0% at 6% PEG [3]	Low; sharp decline [3]	Reduced or unchanged [3]	Severe disruption of sap ion concentration [8]

Table 3: Stress Tolerance Indices of Selected F6 Tomato Lines under Drought (Adapted from Source Data [22])

Genotype Line	Stress Tolerance Index (STI) Ranking	Key Adaptive Traits Observed
MC10.4.5.5	Top Performer	High yield stability, maintained biomass
KM23.3.3.10	Top Performer	Superior photosynthetic maintenance
MC74.12.8.1	Top Performer	Enhanced water use efficiency

Application Notes & Protocols

The following protocols provide standardized methodologies for investigating morphological and physiological adaptations to drought stress in tomato, with integrated guidance for deploying modern sensor systems.

Protocol: Phenotypic Screening for Morphological Adaptations

Objective: To quantitatively assess the morphological adaptations of tomato genotypes during early seedling establishment under controlled osmotic stress.

Materials:

• Seeds of tomato genotypes (including wild and domesticated lines)
• Polyethylene Glycol 6000 (PEG-6000)
• Growth chambers with controlled environment
• Plant growth containers and substrate
• Imaging system (e.g., flatbed scanner, digital camera)
• Software for image analysis (e.g., ImageJ)

Procedure:

• Experimental Setup: Prepare aqueous solutions of PEG-6000 at concentrations of 0% (control), 3% (-0.18 MPa), and 6% (-0.36 MPa) to simulate varying drought intensities [3].
• Germination Assay: Place sterilized tomato seeds on filter paper moistened with the respective PEG solutions in Petri dishes. Arrange in a Completely Randomized Design (CRD) with appropriate replication.
• Data Collection: Monitor and record germination percentage daily. At the end of the germination phase (e.g., 10-14 days), harvest seedlings for trait measurement [3].
• Morphological Trait Measurement:
  • Root and Shoot Length: Measure using a ruler or image analysis software.
  • Biomass Accumulation: Separate roots and shoots, and record fresh and dry weights.
  • Root-to-Shoot Ratio: Calculate as (Root Dry Weight / Shoot Dry Weight). An increase under stress indicates a morphological investment in water foraging [3].
  • Leaf Area: Measure using image analysis of detached leaves or digitally from plant images.

Protocol: Sensor-Assisted Profiling of Physiological Adaptations

Objective: To continuously monitor the dynamic physiological responses of tomato plants to progressive drought stress using integrated sensor technology.

Materials:

• Tomato plants at 5-6 leaf stage
• In vivo biosensors (e.g., Bioristor, FPGA-based smart sensors) [8] [21]
• Portable gas exchange system (e.g., with IRGA)
• Climate-controlled greenhouse or growth chamber
• Data logging and analysis platform

Procedure:

• Sensor Integration: For continuous monitoring, implant a Bioristor sensor into the stem of the tomato plant. This organic electrochemical transistor allows for real-time monitoring of changes in ion composition in the sap stream, which is an early indicator of drought stress [8].
• Induction of Drought Stress: Withhold irrigation from a designated group of plants while maintaining a control group under full irrigation. Monitor soil moisture content to track the progression of stress.
• Real-Time Data Acquisition: The Bioristor system will continuously stream data on the plant's physiological status. Data can be coupled with a high-throughput phenotyping platform for complementary insights [8].
• Gas Exchange Measurements: At key intervals, use a portable gas exchange system to measure leaf-level physiological parameters on fully expanded leaves, including:
  • Net Photosynthesis (Pn): Following the COâ‚‚ exchange method [21].
  • Transpiration Rate (E): Calculated from Hâ‚‚O vapor exchange [21].
  • Stomatal Conductance (gs): A key regulator of plant-water relations [21].
• Data Analysis: Correlate the sensor output (e.g., impedance changes from Bioristor) with the discrete gas exchange measurements. Early drops in stomatal conductance often precede detectable changes in photosynthesis and are a key phyto-indicator of mild drought stress [21].

The following diagram illustrates the integrated workflow of this sensor-assisted protocol.

Protocol: Molecular Profiling of Drought-Responsive Genes

Objective: To identify and validate key molecular players in the drought response of different tomato genotypes.

Materials:

• Plant tissue from control and stressed conditions (e.g., leaf)
• RNA extraction kit
• cDNA synthesis kit
• qRT-PCR system
• Primers for candidate drought-responsive genes (e.g., from meta-analysis [13])

Procedure:

• Meta-Analysis for Candidate Gene Identification: Utilize public transcriptomic databases (e.g., GEO-NCBI) to conduct a meta-analysis for identifying consensus drought-responsive genes (meta-DEGs) in tomato, such as CBL-interacting protein kinase 8 or phospholipase C2 [13].
• Plant Treatment and Sampling: Subject different tomato genotypes to controlled drought stress. Collect tissue samples at multiple time points, flash-freeze in liquid nitrogen, and store at -80°C.
• RNA Extraction and qRT-PCR: Extract total RNA, synthesize cDNA, and perform quantitative RT-PCR using primers for the identified meta-DEGs and reference genes.
• Validation: Analyze the expression patterns of these genes. Tolerant genotypes are expected to show stronger and more rapid induction or repression of key positive regulators of drought tolerance [13].

The Scientist's Toolkit: Essential Research Reagents & Solutions

Table 4: Key Research Reagents and Tools for Drought Adaptation Studies

Item	Function/Application	Justification
PEG-6000	Osmoticum to simulate drought stress in controlled laboratory conditions [3].	Inert, non-penetrating polymer that reliably lowers water potential in growth media without toxic effects.
In vivo Bioristor Sensor	Continuous, real-time monitoring of plant physiological status via sap ion content [8].	Provides immediate detection of drought stress onset before visible symptoms appear, enabling dynamic phenotyping.
FPGA-based Smart Sensor	High-frequency measurement of photosynthetic and transpiration dynamics [21].	Allows for precise, noise-reduced monitoring of gas exchange parameters critical for understanding physiological adaptations.
Drought Tolerance Indices (MGIDI, DRI)	Multi-trait indices to classify genotypes based on performance under stress [3] [22].	Provides a standardized, quantitative framework for selecting resilient genotypes in breeding programs.
Primers for Meta-DEGs	qRT-PCR validation of drought-responsive genes from transcriptomic meta-analysis [13].	Targets a consensus set of genes with confirmed roles in drought response, increasing experimental efficiency and relevance.
Cox-2-IN-31	Cox-2-IN-31, MF:C17H16N6O4S, MW:400.4 g/mol	Chemical Reagent
Br-Val-Ala-NH2-bicyclo[1.1.1]pentane-7-MAD-MDCPT	Br-Val-Ala-NH2-bicyclo[1.1.1]pentane-7-MAD-MDCPT, MF:C36H38BrN5O9, MW:764.6 g/mol	Chemical Reagent

Bridging the gap between the historical resilience of wild tomatoes and the agronomic needs of modern agriculture requires a disciplined, multi-faceted approach. By simultaneously employing detailed morphological screening, sensor-enabled physiological profiling, and targeted molecular analysis, researchers can deconstruct the complex phenomenon of drought tolerance into tractable components. The protocols and toolkit outlined in this application note provide a concrete pathway to identify the genetic and physiological basis of drought adaptation. The integration of these methodologies, particularly the use of continuous, in vivo sensor data, will profoundly accelerate the development of climate-resilient tomato varieties, ensuring food security in the face of increasing water scarcity.

Sensor Technologies in Action: From Wearable Devices to AI-Driven Imaging

The increasing frequency and severity of drought events due to climate change pose a significant threat to global tomato production. A critical need exists for advanced phenotyping tools that can dynamically monitor plant physiological responses to water deficit, enabling the selection of resilient cultivars. Recent breakthroughs in plant wearable sensor technology have opened new frontiers for continuous, non-invasive crop monitoring. The PlantRing system represents a pioneering advancement in this domain—a high-throughput, nano-flexible sensor designed for real-time decoding of plant growth and water relations through organ circumference dynamics [10]. This application note details the implementation of PlantRing technology within tomato drought stress research, providing structured quantitative data, detailed experimental protocols, and essential resource guidance for plant scientists.

Technical Specifications and Performance Metrics of the PlantRing System

The PlantRing system utilizes a novel sensing paradigm based on bio-sourced carbonized silk georgette as its primary strain-sensing material. This material selection confers exceptional mechanical and sensing properties ideal for prolonged field deployment on plants [10].

Table 1: Key Metrological Properties of the PlantRing Sensor

Parameter	Specification	Implication for Plant Research
Strain Detection Limit	0.03% – 0.17% (model-dependent)	High sensitivity to minute organ deformations caused by early water loss.
Maximum Tensile Strain	Up to 100%	Accommodates significant growth and swelling without sensor failure.
Durability	Season-long use	Suitable for long-term monitoring across critical growth stages.
Sensing Principle	Circumference variation	Direct measurement of stem micro-variations linked to water status and growth.

The system's performance is characterized by its high stretchability, which allows it to adapt to a wide range of plant organ sizes and species, including tomato stems and fruits [10]. Its exceptional durability ensures reliable data acquisition throughout extended experimental periods, even under harsh field conditions.

Experimental Implementation for Tomato Drought Stress Research

Sensor Deployment and Data Acquisition Workflow

Implementing the PlantRing system for drought studies involves a structured workflow from sensor installation to data interpretation. The following diagram outlines the key stages:

Protocol: Sensor Installation on Tomato Plants

Objective: To reliably affix the PlantRing sensor to tomato stems for continuous monitoring of circumference variations during drought stress experiments.

Materials:

• PlantRing sensor unit(s)
• Mature tomato plants at target developmental stage (e.g., 5-7 leaf stage)
• Soft mounting straps or biocompatible adhesive
• Data logger or wireless receiver unit

Procedure:

• Pre-deployment Calibration: Prior to installation, calibrate each sensor by recording baseline readings against a set of cylinders with known circumferences.
• Plant Selection: Select genetically uniform plants at a consistent developmental stage to minimize biological variation.
• Sensor Placement: Identify a healthy, straight internode section of the main stem, free from branches or visible damage.
• Mounting: Gently open the ring structure and wrap it around the selected stem segment. Ensure the sensor makes uniform contact with the stem surface without constricting the plant tissue. Secure the sensor using its integrated mounting system, avoiding over-tightening.
• Connection: Connect the sensor to a data logger or ensure a stable wireless connection is established.
• Baseline Recording: Allow the system to stabilize for 24-48 hours while maintaining optimal plant water status. Record this period as the baseline stem circumference.
• Initiate Stress Treatment: Commence the planned drought stress regime, while the sensor continuously records circumference dynamics.

Critical Considerations:

• The sensor should move freely with the stem and not act as a mechanical constraint to growth.
• Protect the sensor connector from direct moisture exposure during irrigation or rainfall.
• For field applications, ensure the data logging unit is weatherproof.

Data Interpretation and Correlation with Physiological Traits

Stem diameter, measured as circumference by PlantRing, is a well-established indicator of plant water status. It exhibits diurnal variation: contraction during the day due to transpirational water loss and replenishment/recovery at night. During drought, this pattern becomes more pronounced, and the overall growth rate declines.

Table 2: Interpretation of Key PlantRing Output Metrics in the Context of Drought Stress

Metric	Normal Conditions	Moderate Drought Stress	Severe Drought Stress
Daily Maximum Contraction	Stable, minimal daily variation	Progressive increase in daily contraction amplitude	Extreme daily contraction, often exceeding 5-10% of baseline circumference [9]
Nocturnal Recovery	Complete or near-complete recovery each night	Incomplete nocturnal recovery	Minimal to no nocturnal recovery, indicating tissue dehydration
Cumulative Growth Rate	Steady, positive growth rate	Significant reduction or cessation of growth rate	Negative growth rate (net stem shrinkage over days)
Stomatal Sensitivity	N/A	Quantifiable threshold for stomatal closure can be derived from the relationship between microclimate and contraction onset [10]	N/A

The high-resolution data from PlantRing has been successfully used to quantify stomatal sensitivity to soil drought—a key trait for selecting drought-tolerant germplasm [10]. Furthermore, its application has revealed hydraulic mechanisms underlying fruit disorders like cracking in tomato and watermelon, linking them to genotype-specific sap flow patterns to the fruiting branches [10].

Integration with Broader Physiological Screening

For a comprehensive assessment of drought tolerance, PlantRing data should be correlated with other established physiological and morphological screening methods.

Table 3: Complementary Drought Phenotyping Methods for Tomato

Method	Parameter Measured	Protocol Summary	Relevance to Drought Tolerance
Polyethylene Glycol (PEG) Screening [23]	Germination rate, seedling vigor under osmotic stress	Expose seeds to 0%, 3%, and 6% PEG-6000 solutions in a controlled environment. Monitor germination percentage and rate, and measure root/shoot biomass after a set period.	Identifies genetic variation in early-stage tolerance; genotypes like NGRCO9569 and Monoprecos show high resilience [23].
Chlorophyll a Fluorescence (OJIP Transient) [7]	Photosystem II (PSII) efficiency and electron transport	Dark-adapt leaves for 20 min. Use a hand-held fluorometer to measure the OJIP curve. Derive parameters like PIABS (Performance Index) and Fv/Fm (maximum quantum yield).	Reveals photochemical impairment under drought; landraces like '260' show stable OJIP profiles indicating resilience [7].
Stem Water Potential (Ψstem) [7]	Plant water status	Encase a leaf in a plastic bag covered with foil for at least one hour prior to midday. Use a pressure chamber to measure the balancing pressure of the bagged leaf.	Provides a direct, quantitative measure of plant water stress intensity.

The relationship between these different levels of plant response—from cellular photosynthesis to whole-organ water status and organ-level growth—can be visualized as follows:

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 4: Key Reagents and Materials for Deploying Wearable Plant Sensors in Drought Research

Item	Function / Utility	Example Application / Note
PlantRing Sensor	Core device for continuous, high-sensitivity monitoring of stem/fruit circumference dynamics.	The primary tool for non-destructive, season-long growth and water status monitoring [10].
Carbonized Silk Georgette	The nano-flexible strain-sensing material within PlantRing.	Provides exceptional detection limit, stretchability, and durability [10].
PEG-6000	Osmoticum to simulate drought stress in controlled laboratory environments.	Used for high-throughput screening of germination and seedling-stage drought tolerance [23].
Portable Chlorophyll Fluorometer	For in-field measurement of Chlorophyll a fluorescence OJIP transients.	Assesses the functional state of Photosystem II, a sensitive indicator of stress [7].
Pressure Chamber	For direct measurement of stem water potential (Ψstem).	Provides a definitive measure of plant water status for correlating with sensor data [7].
Fiber Bragg Grating (FBG)	An alternative sensing technology for monitoring stem elongation and fruit expansion.	Can be encapsulated in silicone to create wearable dumbbell or ring-shaped sensors [24].
Bioristor (OECT Sensor)	An implantable sensor for monitoring sap ion composition in real-time.	Detects changes in plant water and nutrient status directly from the xylem sap [9].
Cdc2 kinase substrate	Cdc2 kinase substrate, MF:C53H95N19O12, MW:1190.4 g/mol	Chemical Reagent
Syk-IN-8	Syk-IN-8, MF:C23H26N10, MW:442.5 g/mol	Chemical Reagent

The PlantRing system represents a paradigm shift in plant phenotyping, moving from sporadic, manual measurements to continuous, automated monitoring of critical physiological parameters. Its application in tomato drought research provides unprecedented insights into the dynamics of plant water relations and growth under stress. The high-throughput capability of this technology enables large-scale screening for stomatal sensitivity and other drought-resilience traits, accelerating the selection of superior germplasm. By integrating PlantRing data with complementary physiological screening methods such as chlorophyll fluorescence and stem water potential, researchers can build a holistic understanding of drought tolerance mechanisms. This multi-faceted approach is pivotal for developing climate-resilient tomato cultivars suited for sustainable agriculture in water-limited environments.

Hyperspectral Imaging for Multi-Feature Drought Stress Identification

Hyperspectral Imaging (HSI) has emerged as a transformative technology in precision agriculture, enabling non-destructive and early detection of plant stress responses. For tomato drought stress research, HSI captures subtle physiological changes by measuring reflectance across hundreds of contiguous spectral bands, typically spanning the visible (400-700 nm), near-infrared (NIR, 700-1100 nm), and short-wave infrared (SWIR, 1100-2500 nm) regions [25]. Unlike traditional broadband sensors, HSI generates a complete spectral signature for each pixel in an image, creating a three-dimensional data cube (x, y, λ) that contains both spatial and spectral information [26]. This rich dataset allows researchers to detect drought stress before visible symptoms appear, through quantifiable changes in pigment composition, leaf water content, and canopy structure [26].

The application of HSI is particularly valuable for tomato research because drought stress induces multifaceted physiological responses including stomatal closure, reduced photosynthetic activity, osmotic adjustment, and altered canopy development. Traditional drought assessment methods rely on destructive sampling or visual scoring, which are labor-intensive, subjective, and lag behind the initial physiological responses. HSI addresses these limitations by providing rapid, non-contact measurements that can be deployed at multiple scales—from leaf-level analyzers to unmanned aerial vehicles (UAVs) for field phenotyping [26]. Recent advances in sensor miniaturization and machine learning have further enhanced HSI's capability to identify critical spectral features linked to drought adaptation traits, making it an indispensable tool for tomato breeding programs and precision irrigation management.

Technical Specifications and Research Applications

Table 1: Key Technical Specifications of Hyperspectral Imaging Systems for Plant Stress Monitoring

Component	Specification Options	Research Applications	Considerations
Spectral Range	Visible-NIR (400-1000 nm) [27] [28]	Pigment analysis, chlorophyll detection [26]	Lower equipment cost, limited to pigment-related stress
	NIR-SWIR (957-1677 nm) [29]	Water content, leaf structure, biochemical composition [26]	Better penetration, superior for water stress detection
Spectral Resolution	1-10 nm [27]	Detailed spectral feature identification	Higher data volume, requires more processing
Spatial Resolution	Sub-mm to cm/pixel [26]	Leaf-level to canopy-level monitoring	Determined by sensor distance and optics
Imaging Platform	Laboratory systems [28]	Controlled environment phenotyping	Maximum stability, minimal environmental noise
	UAV-mounted systems [26]	Field-scale screening and mapping	Enables high-throughput phenotyping
Key Performance Metrics	Signal-to-noise ratio, spectral calibration accuracy	Determination of detection sensitivity	Critical for reproducible research

Table 2: Hyperspectral Drought Stress Indicators in Tomatoes

Physiological Parameter	Spectral Region	Specific Wavelengths/Bands	Relationship to Drought Stress
Chlorophyll Content	Visible (500-680 nm) [26]	Red edge (~700-730 nm) [26]	Decreased with stress severity; affects photosynthetic capacity
Water Content	NIR-SWIR (900-1700 nm) [26]	970 nm, 1200 nm, 1450 nm [26]	Strong water absorption features; directly quantifies leaf water status
Leaf Structure	NIR (750-1300 nm) [26]	Broad plateau response	Altered canopy architecture and cell structure
Carotenoid Pigments	Visible (400-550 nm) [26]	~470 nm, ~500 nm [26]	May increase relative to chlorophyll during mild stress

Experimental Protocols for Drought Stress Identification

Hyperspectral Image Acquisition Protocol

Purpose: To acquire high-quality hyperspectral data from tomato plants under controlled drought stress conditions.

Materials and Reagents:

• Push-broom hyperspectral camera (e.g., PIKA XC, Resonon Inc. [27] or ImSpector V10E [28])
• Stable illumination system (e.g., four 15W 12V light bulbs [27] or UV light source [28])
• Translation stage for laboratory systems [28]
• Standard reference panel (≥99% reflectance) for calibration [28]
• Dark reference material (≥99% absorption)
• Environmental control chamber for plant growth
• Data acquisition computer with specialized software

Procedure:

• Plant Preparation: Grow tomato plants under controlled conditions. Implement drought stress treatment by withholding irrigation while maintaining control plants at optimal soil moisture levels.
• System Calibration: Pre-heat the hyperspectral imaging system for 30 minutes prior to data acquisition to stabilize the sensor [28]. Acquire dark reference image (Rdark) with lens completely covered and white reference image (Rwhite) using standard reference panel.
• Image Acquisition: Position plants or leaves ensuring perpendicular orientation to the sensor. For push-broom systems, maintain constant speed (e.g., 5.2134 mm/s [28]) during scanning. Use consistent camera settings (exposure time: 10.5 ms [28], distance: 20-40 cm [27] [28]).
• Data Correction: Apply radiometric correction to raw images (Rraw) using the formula: Rc = (Rraw - Rdark)/(Rwhite - Rdark) [28] [30].
• Region of Interest (ROI) Selection: Define ROIs using image segmentation techniques to exclude background and focus on leaf tissue. For tomato fruits, avoid highlighted regions caused by convex surfaces [27].

Spectral Data Preprocessing Protocol

Purpose: To prepare hyperspectral data for analysis by reducing noise and enhancing features.

Materials and Reagents:

• Computational software (Python, MATLAB, ENVI, or similar)
• Standard normal variate (SNV) transformation algorithms
• Savitzky-Golay smoothing filters
• Derivative analysis tools

Procedure:

• Spectral Extraction: Extract mean spectral values from all pixels within each ROI to represent the sample spectrum [28].
• Noise Reduction: Apply Savitzky-Golay smoothing to reduce high-frequency noise while preserving spectral features [31].
• Scatter Correction: Implement Standard Normal Variate (SNV) transformation or Multiplicative Scatter Correction (MSC) to minimize light scattering effects [29] [28].
• Spectral Enhancement: Calculate first and second derivatives of spectra to resolve overlapping absorption features and enhance subtle spectral variations [29].
• Data Partitioning: Divide dataset into training, validation, and test sets (typical ratio: 7:1:2 [27]) for model development.

Workflow for Hyperspectral Analysis of Drought Stress in Tomatoes

Data Analysis and Modeling Approaches

Feature Selection and Vegetation Indices

Purpose: To identify the most informative spectral features for drought stress identification and reduce data dimensionality.

Traditional Vegetation Indices:

• Normalized Difference Vegetation Index (NDVI): Limited for early drought detection but useful for monitoring advanced stress [26]
• Normalized Difference Water Index (NDWI): Sensitive to leaf water content but may miss early physiological changes [26]

Advanced Machine Learning-Based Indices:

• Machine Learning-Based Vegetation Index (MLVI): Developed using Recursive Feature Elimination (RFE) to identify optimal band combinations from NIR, SWIR1, and SWIR2 regions [26]
• Hyperspectral Vegetation Stress Index (H_VSI): Specifically designed for early stress detection by targeting subtle spectral shifts in water-sensitive regions [26]

Procedure:

• Band Selection: Apply Recursive Feature Elimination (RFE) to identify wavelengths most responsive to drought stress [26].
• Index Formulation: Develop novel indices by combining identified critical bands using mathematical operations (ratios, differences, normalized differences).
• Validation: Correlate new indices with ground-truth physiological measurements (e.g., leaf water potential, relative water content, chlorophyll fluorescence).

Machine Learning and Deep Learning Modeling

Purpose: To develop accurate predictive models for drought stress severity classification using hyperspectral data.

Table 3: Comparison of Modeling Approaches for Hyperspectral Drought Stress Detection

Model Type	Example Algorithms	Accuracy/R² Performance	Advantages	Limitations
Traditional ML	Partial Least Squares Regression (PLSR) [29] [31]	R²: 0.67-0.87 for quality traits [31]	Interpretability, works with smaller datasets	Limited capacity for complex spectral features
	Support Vector Regression (SVR) [29] [28]	R²: 0.965 for lycopene prediction [28]	Effective in high-dimensional spaces	Sensitive to parameter tuning
Deep Learning	1D Convolutional Neural Networks (1D-CNN) [27] [26]	83.4% accuracy for stress classification [26]	Automatic feature extraction, high accuracy	Requires large datasets, computationally intensive
	ResNet [27] [29]	26.4-33.7% improvement over traditional methods [27]	Handles complex patterns, state-of-the-art performance	Black box nature, difficult interpretation
	Transformer Models [29]	R² up to 0.96 [29]	Captures long-range dependencies in spectra	High computational requirements
	Recurrent Neural Networks (RNN) [31]	>40% higher accuracy than RF for maturity classification [31]	Models sequential spectral dependencies	Training complexity

Procedure for Deep Learning Model Development:

• Data Preparation: Format spectral data as 1D vectors (spectral signatures) with corresponding stress level labels.
• Architecture Design: Implement 1D-CNN with convolutional layers for local feature extraction, followed by fully connected layers for classification/regression [27] [26].
• Model Training: Utilize Bayesian optimization for automated hyperparameter tuning [29]. Apply regularization techniques (dropout, batch normalization) to prevent overfitting.
• Model Interpretation: Employ Gradient-weighted Class Activation Mapping (Grad-CAM) to identify wavelengths driving model decisions and enhance interpretability [29].
• Validation: Perform k-fold cross-validation and external validation to assess model generalizability across different tomato cultivars and environmental conditions.

Data Analysis Pathways for Hyperspectral Drought Stress Detection

Implementation and Integration in Research Programs

Scaling from Laboratory to Field Applications

Purpose: To translate hyperspectral protocols from controlled environments to field-scale phenotyping.

UAV Integration:

• Mount compact hyperspectral sensors on UAV platforms for high-throughput field screening [26]
• Implement GPS and inertial measurement units for spatial referencing and image geo-rectification
• Conduct flights during optimal illumination conditions (10:00-14:00 solar time) to minimize shadow effects

Multi-Scale Validation:

• Establish correlation between laboratory measurements and UAV-acquired data
• Implement ground-truthing with traditional physiological measurements (e.g., leaf water potential, stomatal conductance)
• Develop cultivar-specific calibration models to account for genetic variation in spectral responses

The Researcher's Toolkit: Essential Materials and Reagents

Table 4: Essential Research Toolkit for Hyperspectral Drought Stress Studies

Category	Specific Items	Function/Application	Technical Considerations
Imaging Equipment	Push-broom hyperspectral camera [27] [28]	Data acquisition across spectral range	Consider spectral range, resolution, and SNR
	Stable illumination system [27] [28]	Consistent lighting conditions	Critical for reproducible measurements
	Translation stage [28]	Laboratory-based image scanning	Enables push-broom imaging of static samples
Calibration Materials	Standard reference panel [28]	White reference for radiometric calibration	≥99% reflectance preferred
	Dark reference material [28]	Dark current correction	≥99% absorption required
Data Processing	ENVI, Python, MATLAB, or similar [27]	Data preprocessing and analysis	Open-source options available for cost efficiency
Validation Tools	Portable photosynthesis system	Stomatal conductance and photosynthetic rate	Ground-truth validation
	Pressure chamber	Leaf water potential measurements	Direct measurement of plant water status
	Chlorophyll fluorimeter	PSII efficiency and quantum yield	Assessment of photosynthetic apparatus
Lsd1-IN-25	Lsd1-IN-25, MF:C32H33ClN6O3S, MW:617.2 g/mol	Chemical Reagent	Bench Chemicals
Mal-cyclohexane-Gly-Gly-Phe-Gly-Exatecan	Mal-cyclohexane-Gly-Gly-Phe-Gly-Exatecan, MF:C55H60FN9O13, MW:1074.1 g/mol	Chemical Reagent	Bench Chemicals

Hyperspectral imaging provides a powerful, non-destructive methodology for multi-feature drought stress identification in tomato research. The integration of advanced machine learning approaches with high-dimensional spectral data enables early stress detection, severity quantification, and predictive modeling of plant responses. The protocols outlined in this document establish a standardized framework for implementing HSI in drought stress phenotyping, from controlled environment studies to field-scale applications.

Future research directions should focus on developing cultivar-specific spectral libraries, improving model transferability across growth environments, and enhancing real-time processing capabilities for high-throughput phenotyping. The integration of HSI with other sensor technologies (thermal, fluorescence) in multi-sensor fusion approaches will further improve drought stress detection accuracy and provide more comprehensive understanding of plant responses to water limitation. As these technologies continue to advance, HSI will play an increasingly critical role in developing climate-resilient tomato varieties and optimizing irrigation management strategies for sustainable tomato production.

The increasing frequency of drought events due to climate change poses a significant threat to agricultural productivity, making the development of efficient water management strategies imperative. This document details the application of in-vivo sap sensors, specifically the Bioristor, a novel Organic Electrochemical Transistor (OECT), for the real-time monitoring of drought stress responses in tomato plants. By providing a continuous, in-vivo analysis of the ionic composition of xylem sap, the Bioristor enables researchers to detect the onset of water stress long before visible symptoms occur. These application notes and protocols are designed to facilitate the implementation of this sensor technology within a research framework aimed at improving irrigation scheduling and enhancing crop sustainability.

Sensor Operating Principle and Configuration

The Bioristor functions as an Organic Electrochemical Transistor (OECT) that is directly inserted into the plant stem, allowing it to interact with the apoplastic fluid [32]. Its operational principle is based on the modulation of the electrical conductivity of a conductive polymer channel (typically PEDOT:PSS) by ions present in the plant sap [33].

• Ion-Sensitive Detection: When a positive voltage is applied to the gate electrode, cations (e.g., K⁺, Na⁺, Ca²⁺) from the xylem sap are driven into the PEDOT:PSS channel. This influx of cations de-dopes the polymer, reducing its conductivity and consequently decreasing the current flowing from the drain to the source (I_ds) [33]. The magnitude of the change in I_ds is proportional to the concentration of ions in the sap, providing a real-time electrochemical signal of the plant's physiological status.
• System Saturation Monitoring: The current flowing from the gate to the source (I_gs) is also monitored. A reduction in I_gs can indicate lower water availability and a decrease in the system's saturation, which is a direct indicator of drought stress onset [33].

Table 1: Standard Operational Parameters for Bioristor in Tomato Plants

Parameter	Symbol	Typical Value	Function
Drain-Source Voltage	Vds	-0.1 V	Creates a potential difference across the transistor channel.
Gate Voltage	Vg	+0.5 V	Drives cations from the sap into the conductive polymer channel.
Drain-Source Current	Ids	Monitored	Indicates ion concentration in the sap; decreases upon cation influx.
Gate-Source Current	Igs	Monitored	Reflects system saturation and water availability.

Application Notes for Drought Stress Detection in Tomatoes

Key Findings and Performance Data

Research has demonstrated the Bioristor's high sensitivity to early drought stress in tomatoes. In controlled environments, the sensor has detected water stress within 24-30 hours after irrigation was stopped, a point at which the substrate water content was approximately 50% of the well-watered control [34] [32]. This early warning is provided well before the manifestation of visible wilting or significant changes in other parameters like sap flow or PSII quantum yield [34].

The sensor's output shows characteristic diurnal oscillations that vary with plant growth stages and fruit maturation. During drought stress, these oscillations are dampened, and the overall signal trend reflects the reduced ionic content and saturation of the system, allowing for the calculation of a sensor response index (R) that strongly correlates with environmental parameters like vapor pressure deficit (VPD) [33].

Table 2: Comparative Analysis of Plant Sensors for Drought Stress Detection in Tomatoes

Sensor Type	Measured Parameter	Reaction to Early Drought Stress	Key Advantage	Key Limitation
Bioristor (OECT)	Ionic composition of xylem sap	Clear indicator (within 24-30 hrs) [34] [33]	Continuous, real-time, in-vivo monitoring; very early detection.	Invasive (requires stem insertion).
Acoustic Emissions	Cavitation events in xylem	Clear indicator [34]	Detects xylem embolism formation.	Does not measure pre-embolism stress.
Stem Diameter	Micro-variations in stem size	Clear indicator [34]	Simple, well-established technique.	Can be influenced by other growth factors.
Stomatal Conductance	Stomatal pore area & conductance	Clear indicator [34]	Direct measure of gas exchange.	Point measurement, influenced by microclimate.
Sap Flow	Rate of water movement in xylem	No clear sign in early stages [34]	Integrative measure of plant water use.	Lags behind leaf water status.
Chlorophyll Fluorescence	PSII quantum yield	No clear sign in early stages [34]	Measures photosynthetic efficiency.	Not sensitive to initial water deficit.

Experimental Protocol: Bioristor Installation and Data Acquisition

This protocol describes the procedure for implanting a Bioristor sensor into the main stem of a tomato plant for continuous sap analysis.

Materials and Reagents

Table 3: Research Reagent Solutions and Essential Materials

Item Name	Function / Application
Bioristor Sensor	Functionalized textile thread (e.g., polypropylene) coated with conductive polymer PEDOT:PSS [33].
PEDOT:PSS (Clevios PH1000)	Conductive polymer that forms the active channel of the OECT; its de-doping by sap ions generates the signal [33].
Dodecyl Benzene Sulfonic Acid	Additive to the PEDOT:PSS solution to improve homogeneity and adhesion [33].
Sulfuric Acid (Hâ‚‚SOâ‚„), 95%	Used in post-deposition treatment to enhance the crystallinity and electrical properties of the polymer [33].
Plasma Oxygen Cleaner	Used to clean and functionalize the textile thread substrate, improving polymer wettability and adhesion [33].
IoT Control Unit (e.g., Arduino DUE)	Powers the sensor, reads electrical signals (Ids, Igs), and transmits data to a cloud server or local storage [33].
Micro-weather Unit (e.g., DHT11)	Monitors ambient temperature and relative humidity to correlate sensor data with environmental conditions [33].

Step-by-Step Procedure

• Sensor Fabrication and Preparation:

  • Clean polypropylene textile threads using an oxygen plasma cleaner to remove impurities and enhance wettability [33].
  • Prepare an aqueous solution of the conductive polymer PEDOT:PSS (Clevios PH1000) with 2% (v/v) dodecyl benzene sulfonic acid [33].
  • Deposit the polymer solution onto the threads via drop-casting (50 µL/cm) and treat them with concentrated sulfuric acid (95%) for 20 minutes to improve electrical properties [33].
  • Rinse the threads with water and anneal at 130°C for 1 hour to finalize the sensor [33].

• Sensor Implantation:

  • Select a robust, mature tomato plant. Choose an implantation site on the main stem, avoiding nodes and visible vascular bundles.
  • Using a 0.8 mm drill bit, carefully create a hole through the stem [33].
  • Insert the functionalized textile thread (the transistor channel) through the stem. Connect both ends of the channel to metal wires, which will act as the source and drain electrodes. Secure the connections with a conductive silver paste [33].
  • Place the gate electrode in proximity to the channel, completing the OECT circuit within the plant.

• System Setup and Data Acquisition:

  • Connect the source, drain, and gate electrodes to a custom read-out electronics board and an IoT control unit (e.g., Arduino DUE) [33].
  • Configure the system to apply constant voltages (V_ds = -0.1 V, V_g = +0.5 V) and record the resulting I_ds and I_gs currents with a sampling rate of 1 Hz [33].
  • Integrate a micro-weather station (e.g., DHT11) to simultaneously record air temperature and relative humidity [33].
  • Ensure data is saved locally and/or transmitted to a cloud server via a 4G connection for remote monitoring [33].

Integration with Other Phenotyping Technologies

The data generated by the Bioristor can be powerfully complemented by non-invasive phenotyping technologies. Deep learning-based image analysis provides a method for quantifying morphological responses to stress.

For instance, an improved YOLOv11n deep learning model has been used to automatically extract phenotypic traits from tomato plants under water stress, achieving a high accuracy (98% classification accuracy using a Random Forest classifier) in differentiating stress levels based on traits like plant height and petiole count [2] [35]. Integrating in-vivo sap data with automated image-based phenotyping creates a robust, multi-modal framework for comprehensive plant stress physiology analysis.

The Bioristor represents a significant advancement in plant sensor technology, moving beyond soil and environmental monitoring to provide a direct, real-time window into the plant's internal physiological state. Its demonstrated ability to provide an early warning of drought stress in tomato plants makes it an invaluable tool for research aimed at understanding plant stress responses and developing data-driven precision irrigation protocols. The integration of this in-vivo sensor data with other high-throughput phenotyping techniques, such as deep learning-based image analysis, paves the way for a more holistic and sustainable approach to crop management in the face of climate change.

Deep Learning and YOLO Models for Automated Phenotypic Trait Extraction

The implementation of sensor systems for drought stress detection in tomato research represents a paradigm shift in precision agriculture. These systems generate vast amounts of image data, creating an urgent need for automated analytical methods. Deep learning, particularly YOLO (You Only Look Once) models, has emerged as a powerful solution for extracting phenotypic traits from this image data, enabling high-throughput, non-destructive monitoring of plant responses to water stress [2] [35]. This approach allows researchers to move beyond traditional manual phenotyping methods, which are labor-intensive, subjective, and impractical for large-scale studies [36] [37].

The integration of deep learning with sensor systems provides a technological foundation for detecting subtle phenotypic changes that serve as early indicators of drought stress. By automatically quantifying morphological traits, these systems offer unprecedented opportunities for understanding plant stress responses and developing targeted irrigation strategies [2]. This technical note details the application of YOLO-based models for phenotypic trait extraction within the broader context of drought stress detection research in tomatoes.

Technical Specifications and Performance Metrics

Advanced YOLO Architectures for Plant Phenotyping

Recent research has demonstrated the effectiveness of modified YOLO architectures in addressing the unique challenges of plant phenotyping. One significant advancement involves the integration of Adaptive Kernel Convolution (AKConv) into the YOLOv11n backbone's C3 module (C3k2) alongside a recalibrated feature pyramid detection head based on the P2 layer [2] [35]. This architectural enhancement specifically improves the detection of small plant structures and multi-scale features that are characteristic of tomato plants under stress conditions.

The improved model demonstrates substantial performance gains over baseline implementations, achieving a 4.1% increase in recall, a 2.7% increase in mAP50, and a 5.4% increase in mAP50-95 for tomato phenotype recognition tasks [2]. These metrics indicate enhanced reliability in detecting relevant phenotypic features while reducing false negatives—a critical requirement for accurate stress detection systems.

Performance Across Phenotypic Traits

Table 1: Performance metrics of YOLO-based models for different phenotypic traits in tomato plants.

Phenotypic Trait	Model Architecture	Performance Metric	Value	Application Context
Plant Height	Improved YOLOv11n	Average Relative Error	6.9%	Drought response monitoring [2]
Petiole Count	Improved YOLOv11n	Average Error	10.12%	Architectural analysis [2]
Nodes, Fruits, Flowers	YOLOv5-based	Detection Accuracy	Relatively High	Stress experiment on multiple genotypes [37]
Stomatal Guard Cells	YOLOv8	Segmentation Accuracy	High	Water loss and gas exchange analysis [36]

For complex structural traits, YOLOv5-based detectors have been successfully applied to identify nodes, fruits, and flowers across multiple tomato genotypes under stress conditions [37]. These models effectively handle challenges including varying object sizes, morphological similarities between organs, and color variations in natural environments. The robust performance across different trait types highlights the versatility of YOLO architectures for comprehensive phenotyping applications.

Experimental Protocols

Protocol 1: YOLO-Based Phenotypic Trait Extraction from Tomato Plants

Objective: To automatically identify and quantify key phenotypic traits (plant height, petiole count, leaf number) from tomato plant images under varying water stress conditions using an improved YOLOv11n model.

Materials and Equipment:

• Tomato plants (Solanum lycopersicum L.) at appropriate growth stages
• Imaging setup: Controlled lighting environment (e.g., 450 ± 100 μmol m⁻² s⁻¹ sunlight)
• Image acquisition system: Digital camera (e.g., 2592 × 1458 pixel resolution) [36]
• Computing hardware: GPU-enabled workstation for model training and inference
• Software: Python environment with PyTorch, YOLOv11n implementation, AKConv module

Procedure:

• Experimental Setup: Conduct potted experiments with controlled water stress treatments. Maintain environmental controls (temperature: 32 ± 2°C, relative humidity: 70 ± 5%) to isolate water stress effects [36].
• Image Acquisition: Capture standardized images of tomato plants against consistent backgrounds. Ensure consistent camera positioning and lighting conditions across all samples.
• Data Annotation: Manually label phenotypic traits of interest (plant height, petioles, leaves) in training images using annotation tools (e.g., Labelme) [36]. Convert annotations to YOLO format.
• Model Configuration: Integrate AKConv into the C3k2 module of YOLOv11n backbone. Implement recalibrated feature pyramid detection head with P2 layer for enhanced small-object detection [2] [35].
• Model Training: Train the improved YOLOv11n model using transfer learning. Apply data augmentation techniques (rotation, scaling, color adjustment) to improve model robustness.
• Trait Extraction: Use trained model to generate bounding boxes for phenotypic traits. Apply post-processing algorithms to calculate plant height from vertical bounding box dimensions and count petioles/leaves from detection counts [2].
• Validation: Compare model-derived measurements with manual measurements to calculate average relative error for each trait.

Troubleshooting Tips:

• For poor small-object detection: Adjust anchor box sizes and increase input image resolution
• For class imbalance: Apply weighted loss functions or oversample rare classes
• For overfitting: Implement more aggressive data augmentation and regularization

Protocol 2: Stomatal Phenotyping Using YOLOv8

Objective: To segment stomatal pores and guard cells from microscope images of tomato leaves for drought response analysis.

Materials and Equipment:

• Microscope slides with leaf impressions
• Inverted microscope (e.g., CKX41) with digital camera (e.g., DFC450) [36]
• Computer with GPU capabilities
• Software: Python, YOLOv8, image processing libraries

Procedure:

• Sample Preparation: Create leaf impressions using cyanoacrylate glue on microscope slides [36].
• Image Acquisition: Capture high-resolution images (2592 × 1458 pixels) of leaf surfaces using microscope and camera system.
• Image Enhancement: Apply Lucy-Richardson deblurring algorithm to improve stomatal boundary definition [36].
• Data Annotation: Manually label stomatal pores and guard cells in training images using instance segmentation masks.
• Model Training: Train YOLOv8 segmentation model on annotated dataset with appropriate learning rate scheduling.
• Trait Extraction: Use trained model to segment stomatal features. Calculate density, size, distribution, and orientation metrics from segmentation masks.
• Advanced Metrics: Compute novel phenotypic features including stomatal angles and opening ratio (guard cell area to pore area) [36].

Troubleshooting Tips:

• For blurry images: Optimize deblurring parameters and check microscope focus
• For inconsistent segmentation: Increase color normalization in preprocessing
• For orientation measurement: Implement ellipse-fitting algorithms on segmentation masks

Workflow Visualization

Diagram 1: Workflow for automated phenotypic trait extraction using YOLO models.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential materials and reagents for implementing deep learning-based phenotyping of drought stress in tomatoes.

Category	Item	Specification	Function/Application
Plant Materials	Tomato Genotypes	Drought-tolerant (NGRCO9569, Khumal 2) and susceptible (Srijana) cultivars [3]	Provide genetic variability for stress response studies
Imaging Equipment	Digital Camera	High-resolution (e.g., 2592 × 1458 pixels) [36]	Image acquisition for phenotypic analysis
Imaging Equipment	Inverted Microscope	With camera attachment (e.g., CKX41 with DFC450) [36]	High-magnification imaging of stomatal features
Stress Induction	Polyethylene Glycol (PEG)	PEG-6000 for osmotic stress simulation [3]	Controlled induction of drought stress in laboratory settings
Computing Resources	GPU Workstation	NVIDIA GPU with CUDA support	Model training and inference acceleration
Software Tools	YOLO Implementation	YOLOv11n with AKConv modification [2] [35]	Core detection architecture for phenotypic traits
Software Tools	Annotation Tools	Labelme, LabelImg [36]	Manual annotation of training datasets
Analysis Software	Image Processing	Python with OpenCV, scikit-image	Preprocessing and feature extraction
Analysis Software	Machine Learning	PyTorch, TensorFlow	Model development and implementation
AC3-I, myristoylated	AC3-I, myristoylated, MF:C78H137N21O20, MW:1689.1 g/mol	Chemical Reagent	Bench Chemicals
Usp1-IN-5	Usp1-IN-5, MF:C27H23F3N8O, MW:532.5 g/mol	Chemical Reagent	Bench Chemicals

Integration with Drought Stress Detection Research

The application of YOLO-based phenotypic trait extraction provides critical data layers for comprehensive drought stress detection systems. When integrated with sensor networks monitoring environmental parameters, these phenotypic measurements enable robust correlation analysis between environmental conditions and plant responses [38]. The automatically extracted traits serve as valuable input features for classification algorithms determining stress severity levels.

Random Forest classifiers utilizing YOLO-extracted phenotypic features have demonstrated exceptional performance in differentiating tomato plants under varying water stress conditions, achieving classification accuracy up to 98% [2]. This integration of computer vision and machine learning creates a powerful framework for early stress detection that surpasses the capabilities of single-modality systems.

Furthermore, the phenotypic data generated through these automated methods contributes to the identification of drought-tolerant genotypes. Studies have shown that genotypes like NGRCO9569 and Khumal 2 maintain higher germination rates and seedling vigor under PEG-induced drought stress compared to susceptible varieties like Srijana [3]. The ability to automatically quantify phenotypic responses enables high-throughput screening of germplasm collections, accelerating the development of drought-resilient tomato cultivars.

Deep learning-based approaches, particularly YOLO models, have transformed phenotypic trait extraction from a manual, low-throughput process to an automated, high-precision operation. The integration of these computer vision techniques with drought stress research creates unprecedented opportunities for understanding plant responses to water limitations at scale and with temporal resolution previously unattainable. As these methodologies continue to evolve, they will play an increasingly vital role in developing climate-resilient agricultural systems and optimizing water management practices in tomato production and beyond.

IoT-Enabled Weight Sensors and Smart Greenhouses for Real-Time Feedback

The integration of Internet of Things (IoT) technology into agricultural systems represents a transformative approach for addressing abiotic stresses, such as drought, in greenhouse cultivation. This document details the application of IoT-enabled weight sensors within smart greenhouses to achieve real-time, non-destructive monitoring of drought stress in tomato plants (Solanum lycopersicum L.). By providing continuous biofeedback on plant physiological status, these systems enable precise irrigation management and support the development of climate-resilient cropping systems [39] [40].

Table 1: Performance Metrics of IoT-Enabled Weight Sensor System for Tomato Drought Stress Analysis

Parameter	Reported Performance/Value	Context & Conditions
LAI Simulation Accuracy	R² = 0.99, NRMSE = 0.04	High consistency between sensor-predicted and actual Leaf Area Index [39].
LAIp Simulation Accuracy	R² = 0.98, NRMSE = 0.04	High consistency for the Photosynthetic Leaf Area Index, a key transpiration variable [39].
Drought Identification Accuracy	95.78% (Prediction Set)	Achieved using multi-feature hyperspectral imaging and subsample fusion, validating multi-sensor approaches [41].
Germination Rate under Osmotic Stress	4.34 (Ratio for NGRCO9569)	Germination rate of a drought-tolerant genotype under PEG-induced drought stress [3].
Power Source for Sensor Nodes	Solar Panels	System designed for continuous operation with minimal energy intervention, suitable for long-term drought studies [40].

Table 2: Drought Tolerance Indices and Performance of Selected Tomato Genotypes

Genotype	Key Drought Tolerance Characteristics	Potential Application in Sensor-Fusion Research
NGRCO9569	Maintained high germination rate and seedling vigor under 6% PEG stress; ranked top performer by MGIDI and DRI indices [3].	Ideal candidate for validating sensor readings against physiological drought tolerance.
Khumal 2	Maintained high germination (93.33%) at 3% PEG; showed lower reduction in germination with increasing stress [3].	Useful for establishing baseline sensor responses for a tolerant commercial variety.
Monoprecos	Maintained statistically similar germination percentage at 3% PEG compared to control [3].	Suitable for studies correlating weight loss dynamics with germination resilience.
F6 Lines (e.g., MC10.4.5.5)	Selected as top-performing lines based on Stress Tolerance Index (STI) under parallel irrigated and drought environments [22].	Advanced breeding lines for testing sensor-based yield prediction under drought.
Srijana	Exhibited sharp decline in all parameters; complete germination failure at 6% PEG [3].	Serves as a sensitive control for calibrating sensor-based early stress detection thresholds.

Experimental Protocols

Protocol: Deployment of an IoT-Enabled Weight Sensor Network for Tomato Drought Stress Monitoring

Objective: To establish a real-time monitoring system for detecting tomato plant drought stress through continuous measurement of shoot and root system weight dynamics and subsequent derivation of physiological indices [39].

Materials:

• IoT-enabled precision weight sensors (e.g., load cells).
• Data acquisition module with microcontrollers (e.g., Arduino, Raspberry Pi).
• Wireless communication modules using LoRaWAN protocol [40].
• Secured sensor enclosures resistant to greenhouse conditions.
• Power supply unit (e.g., solar panels with battery storage) [40].
• Cloud platform or local server for data storage and analysis.
• Potted tomato plants (various genotypes recommended in Table 2).

Methodology:

• System Setup and Calibration:
  • Install weight sensors on a stable platform within the greenhouse.
  • Calibrate each sensor using standard weights to ensure accuracy across the expected measurement range.
  • Integrate the sensor with the data acquisition module and wireless transmitter.

• Plant Establishment and Sensor Integration:

  • Transplant tomato seedlings into pots that will be placed directly on the weight sensors.
  • Ensure proper acclimatization of plants under well-watered conditions before initiating stress treatments.
  • Record the initial tare weight of the pot and substrate.

• Data Acquisition and Transmission:

  • Program the data logger to capture weight measurements at frequent intervals (e.g., every 5-15 minutes).
  • Configure the LoRaWAN modules to transmit data packets to a central gateway at set intervals [40].
  • The gateway relays the data to a cloud-based platform for storage and processing.

• Implementation of Drought Stress:

  • Divide plants into two groups: a well-watered control (maintaining 60-80% Soil Relative Humidity) and a drought-stress group.
  • For the stress group, withhold irrigation to allow soil moisture to decline. Monitor soil moisture manually or with additional sensors to define stress levels (e.g., reduced-watered: 40-60% SRH; deficient-watered: 20-40% SRH) [41].

• Data Processing and Model Application:

  • Data Cleaning: Filter raw weight data for noise caused by environmental factors like wind.
  • Transpiration Modeling: Apply a canopy transpiration model (e.g., WEP-based model) trained on the real-time weight and environmental data [39]. The diurnal weight loss is primarily attributed to plant transpiration.
  • Parameter Inversion: Use the trained model to invert weight signal dynamics into key growth parameters such as Leaf Area Index (LAI) and the Photosynthetic Leaf Area Index (LAIp) non-destructively and in real-time [39].

• Validation:

  • Periodically destructively harvest a subset of plants to measure actual LAI and biomass for validating the sensor-derived LAI and LAIp values [39].
  • Correlate sensor-derived transpiration rates and indices with visual symptoms and other physiological measurements (e.g., stomatal conductance, leaf water potential).

Protocol: High-Throughput Phenotyping of Tomato Genotypes for Drought Tolerance Using PEG-Induced Osmotic Stress

Objective: To rapidly screen and identify drought-resilient tomato genotypes under controlled laboratory conditions using polyethylene glycol (PEG) to simulate soil moisture deficit, providing a validated phenotype for sensor system correlation [3].

Materials:

• Seeds of tomato genotypes (see Table 2 for recommended lines).
• Polyethylene Glycol 6000 (PEG-6000).
• Germination papers or agar plates.
• Growth chambers with controlled temperature and light.
• Laminar flow hood for sterile work.
• Equipment for measuring seedling traits (ruler, precision balance, scanner).

Methodology:

• Solution Preparation:
  • Prepare aqueous solutions of PEG-6000 at concentrations of 0% (control), 3% (-0.18 MPa), and 6% (-0.36 MPa) to simulate varying degrees of osmotic stress [3].

• Seed Sterilization and Planting:

  • Surface-sterilize tomato seeds.
  • Place a set number of seeds (e.g., 20-25 per replicate) on germination papers or agar plates saturated with the respective PEG solutions or distilled water (control).

• Incubation and Data Collection:

  • Arrange treatments in a Completely Randomized Design (CRD) within a growth chamber.
  • Germination Percentage: Record the number of germinated seeds daily until germination stabilizes. Calculate final germination percentage.
  • Germination Rate: Calculate using a standard formula (e.g., Maguire's formula) based on daily counts.
  • Seedling Traits: After a set period (e.g., 14 days), measure root and shoot length, seedling fresh weight, and then dry weight. Calculate derived indices like Vigor Index and Root-to-Shoot Ratio [3].

• Statistical Analysis and Selection:

  • Perform Analysis of Variance (ANOVA) to detect significant effects of genotype, treatment, and their interaction.
  • Compute drought tolerance indices such as the Drought Resistance Index (DRI) and Multi-Trait Genotype-Ideotype Distance Index (MGIDI) to rank and select the most resilient genotypes [3].

Visualization of System Workflow and Data Logic

The following diagram illustrates the integrated workflow of the IoT sensor system and the parallel genetic screening protocol, highlighting how they inform each other.

IoT and Genetics Workflow for Drought Research

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 3: Key Research Reagents and Materials for IoT-Based Drought Stress Studies in Tomatoes

Item	Function/Application	Specific Examples/Notes
IoT-Enabled Weight Sensor	Core component for continuous, non-destructive monitoring of plant weight, the primary signal for deriving transpiration and growth.	High-precision load cells; System should be capable of capturing dynamic diurnal changes [39].
LoRaWAN Communication Module	Enables long-range, low-power wireless data transmission from sensors to a central gateway, ideal for greenhouse environments.	Key for remote, real-time data acquisition; superior for energy efficiency and range compared to Wi-Fi [42] [40].
Polyethylene Glycol (PEG) 6000	An inert osmotic agent used in laboratory screening to simulate soil water deficit and induce controlled, reproducible drought stress.	Used at 3% (-0.18 MPa) and 6% (-0.36 MPa) concentrations to screen genotypes at the germination and seedling stages [3].
Drought-Tolerant Tomato Genotypes	Validated plant material essential for testing, calibrating, and deriving meaningful physiological models from sensor data.	Genotypes such as NGRCO9569, Khumal 2, and advanced breeding lines (e.g., MC10.4.5.5) [3] [22].
Hyperspectral Imaging (HSI) System	Provides spatial and spectral data for non-destructive assessment of plant physiological status, used to validate and fuse with weight sensor data.	Can identify drought stress through spectral signatures in leaves; achieved 95.78% prediction accuracy when fused with other features [41].
Ido1-IN-23	Ido1-IN-23, MF:C20H27N3O2S, MW:373.5 g/mol	Chemical Reagent

Overcoming Deployment Challenges: Signal Processing and Model Optimization

Addressing Environmental Noise in Complex Field Conditions

Implementing sensor systems for drought stress detection in tomato cultivation requires robust strategies to mitigate environmental noise, which can significantly compromise data quality and interpretation. Field conditions introduce numerous variables—from fluctuating climate parameters to spatial heterogeneity—that can obscure genuine plant physiological responses. This application note details validated protocols and analytical techniques to isolate drought stress signals from environmental interference, enabling more reliable sensor-based monitoring for research and development applications.

The following tables consolidate performance data and characteristics for prominent sensor technologies used in tomato drought stress research under field conditions.

Table 1: Performance Comparison of Drought Stress Detection Sensor Technologies

Sensor Technology	Key Measured Variable(s)	Reported Water Saving	Noise Handling Capability	Implementation Complexity
FPGA-based Smart Sensor [21]	Photosynthesis (Pn), Transpiration (E), Stomatal Conductance (gs)	Not Quantified	Discrete Wavelet Transform for high-frequency noise rejection	High (requires digital signal processing expertise)
Bioristor (OECT-based) [9]	Sap ion composition (Index R)	Up to 36%+	Real-time, in vivo sensing reduces spatial noise	Medium (requires sensor implantation)
Terahertz Spectroscopy [43]	Leaf water content (Power, Absorbance, Transmittance)	Not Quantified	Savitzky-Golay smoothing for noise reduction; fusion models improve accuracy	High (specialized spectroscopic equipment)
IoT-based System [44] [45]	Soil moisture, canopy cover, environmental parameters	Up to 40%	Data fusion from multiple sources; index-based spatial variation compensation	Medium (requires sensor network deployment)

Table 2: Signal Processing Techniques for Noise Mitigation

Processing Technique	Application Context	Function	Reported Effectiveness
Discrete Wavelet Transform (DWT) [21]	FPGA-based gas exchange data	Digital filtering to reject high-frequency noise while preserving physiological signal	Enabled detection of drought conditions independent of greenhouse climate variations
Index-Based Methodology [21]	Spatial variation in greenhouses	Compensates for microclimate differences across growing environments	Successfully determined treatment differences independent of internal climate variations
Savitzky-Golay Smoothing [43]	Terahertz spectral data	Reduces interference and noise in spectral measurements while preserving data features	Improved model accuracy for leaf moisture content prediction
Kalman Filtering [21]	Photosynthesis measurement systems	Combined with average decimation to improve signal quality	Part of processing chain that enabled reliable drought detection

Experimental Protocols

FPGA-Based Smart Sensor Deployment with DWT Noise Filtering

Objective: Implement a field-deployable sensor system capable of detecting drought stress through gas exchange measurements while rejecting environmental noise through digital signal processing.

Materials:

• FPGA development board with analog input capabilities
• Infrared Gas Analyzer (IRGA) COâ‚‚ sensors
• Environmental sensors (air temperature, humidity, PAR)
• Custom leaf chamber with temperature control
• Computer with FPGA programming tools

Methodology:

• Sensor Calibration:
  • Calibrate COâ‚‚ sensors using known concentration standards before deployment
  • Verify temperature and humidity sensor accuracy against reference instruments
  • Characterize sensor response times to optimize sampling rates

• System Deployment:

  • Install sensors on tomato plants representing different irrigation treatments
  • Position leaf chambers on fully expanded, sun-exposed leaves
  • Implement continuous data acquisition at 1 Hz sampling frequency

• Signal Processing Implementation:

  • Apply average decimation to reduce high-frequency noise
  • Implement Kalman filtering for state estimation and prediction
  • Configure Discrete Wavelet Transform as final filtering stage:
    • Select appropriate wavelet basis function (e.g., Daubechies)
    • Determine optimal decomposition level for physiological signals
    • Apply thresholding to wavelet coefficients to remove noise components

• Data Validation:

  • Compare processed signals with manual photosynthesis measurements
  • Verify drought detection capability across irrigation treatments
  • Correlate sensor outputs with destructive plant water status measurements

Troubleshooting:

• Signal drift may require periodic baseline correction
• Leaf chamber sealing issues manifest as rapid parameter fluctuations
• FPGA resource constraints may necessitate optimization of wavelet implementation

Bioristor Implantation for Continuous Sap Monitoring

Objective: Monitor real-time changes in tomato sap composition in response to drought stress using implantable OECT-based sensors, minimizing environmental interference through direct plant tissue measurement.

Materials:

• Bioristor sensors and control units
• Sterilized implantation tools
• Data logging system with weatherproof enclosure
• Reference plants for destructive measurements
• Drip irrigation system with precision controllers

Methodology:

• Sensor Preparation:
  • Functionalize textile fibers (polypropylene) serving as channel and gate
  • Calibrate sensors in standard solutions before implantation
  • Verify sensor stability through continuous operation testing

• Implantation Procedure:

  • Select stem region between second and third fully expanded leaf
  • Sterilize implantation site with ethanol solution
  • Insert sensor fibers into stem tissue using specialized guide
  • Secure sensor position without constricting stem growth
  • Verify electrical connections and signal stability

• Field Deployment:

  • Install control units with weather protection
  • Establish continuous data recording with timestamp synchronization
  • Implement differential irrigation regimes (e.g., 100%, 70%, 50% of recommended)
  • Monitor sensor response index (R) throughout growing season

• Data Interpretation:

  • Normalize sensor response to pre-treatment baseline
  • Correlate R index with environmental parameters (VPD, temperature)
  • Establish threshold values indicating drought stress onset
  • Compare temporal patterns with plant physiological measurements

Validation Metrics:

• Sensor response correlation with leaf water potential
• Timing of stress detection compared to visual symptoms
• Water use efficiency calculations based on irrigation adjustments

Visualization of Methodologies

Signal Processing Workflow for FPGA-Based System

Integrated Field Deployment Strategy

Research Reagent Solutions

Table 3: Essential Research Materials for Drought Stress Sensor Systems

Category	Specific Item	Function/Application	Key Characteristics
Sensor Platforms	FPGA Development Board [21]	Implementation of real-time signal processing algorithms	Parallel computation capability, flexible configurability
	OECT-based Bioristor [9]	In vivo monitoring of sap composition changes	Real-time, continuous operation; implantable in stems
	Terahertz Time-Domain Spectrometer [43]	Non-destructive leaf water content measurement	Extreme sensitivity to water; fingerprinting capability
Signal Processing	Discrete Wavelet Transform Library [21]	Digital filtering of high-frequency noise	Multi-resolution analysis, ideal for physiological signals
	Savitzky-Golay Algorithm [43]	Spectral data smoothing	Noise reduction while preserving spectral features
	Kalman Filter Implementation [21]	State estimation and prediction	Optimal recursive data processing algorithm
Validation Tools	Infrared Gas Analyzer (IRGA) [21]	Reference measurement of photosynthesis and transpiration	Gold standard for gas exchange measurements
	Pressure Chamber [9]	Measurement of leaf water potential	Direct assessment of plant water status
	Soil Moisture Sensors [44] [45]	Continuous monitoring of root zone water availability	Complementary data for irrigation decision support

Effective management of environmental noise is fundamental to deploying reliable sensor systems for drought stress detection in tomato crops. The integration of plant-centric sensing technologies with advanced signal processing approaches—particularly Discrete Wavelet Transform and real-time data fusion—enables researchers to extract meaningful physiological signals from noisy field data. The protocols and methodologies detailed herein provide a framework for implementing robust sensor networks capable of generating high-quality data for both research and precision irrigation applications, ultimately contributing to more sustainable water management in agricultural systems.

Optimizing Deep Learning Models for Small-Target Detection in Dense Canopies

The implementation of sensor systems for the early detection of drought stress in tomatoes represents a significant advancement in precision agriculture. A pivotal component of this system is the reliable automated detection of subtle, early-stage physiological changes within the complex visual environment of a plant canopy. This task is formalized as a small-target detection problem in computer vision, where the "targets" are early-stress indicators such as slight leaf wilting, minor changes in leaf angle, or subtle color variations. These indicators are often visually small, occupying minimal pixel area in images or video streams captured by drones or fixed sensors [46]. In dense canopies, these challenges are exacerbated by occlusion, complex backgrounds, and lighting variations, making direct application of standard deep learning models suboptimal [46]. This document outlines application notes and experimental protocols for optimizing deep learning models to overcome these specific challenges, enabling robust, early detection of drought stress.

Deep Learning Optimization Strategies for Small Targets

Optimizing detection models for the unique conditions of agricultural canopies requires targeted architectural adjustments and data preparation strategies. The following approaches have proven effective in addressing the core challenges of small targets, dense occlusion, and complex backgrounds.

• Image Super-Resolution Preprocessing: Before processing by the detection network, input images can be super-resolved using models like the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN). This preprocessing step enhances the resolution and detail of small target features, such as the venation of a single leaf or a small wilting patch, providing the subsequent detection network with more discriminative information and significantly improving detection accuracy [47].

• Multi-Scale Feature Enhancement and Aggregation: Standard detectors often lose fine-grained features of small targets through successive pooling and striding layers. To counter this, integrate Multi-Scale Feature Enhancement Modules (MFEB) or self-designed Multi-Scale Feature Aggregation (MSFA) modules into the network's backbone. These modules use parallel convolutional pathways with different receptive fields (e.g., standard, dilated convolutions) to capture feature information at multiple scales. This allows the network to understand both the local context of a small wilting region and its broader position within the leaf and plant structure, reducing false alarms from background noise [47] [48].

• Enhanced Feature Fusion Networks: Replacing standard convolutional layers with more adaptable variants, such as snake convolution, can dynamically adjust the effective receptive field of the network. This flexibility is crucial for capturing the irregular shapes and appearances of small, stress-related targets in canopies without being constrained by a fixed rectangular grid, leading to improved feature extraction for small, dense objects [48].

• Dynamic Detection Heads and Optimized Loss Functions: The prediction head of the network can be augmented with a dynamic detection head (DyHead) that incorporates scale, spatial, and task attention mechanisms. This head dynamically fuses features from different scales according to their semantic importance for the detection task. Furthermore, replacing standard IoU-based loss functions with improved variants like wise-IoU can help by dynamically adjusting the loss contribution during training, leading to more stable convergence and superior localization accuracy for small bounding boxes around early-stress indicators [47] [48].

Experimental Protocols for Model Training and Validation

A rigorous, repeatable experimental protocol is essential for developing and validating high-performance detection models.

Data Acquisition and Preprocessing Protocol

Objective: To collect and prepare a high-quality dataset of tomato canopy images under varying drought stress conditions for model training and testing.

Materials:

• Plant Material: Multiple tomato genotypes (e.g., Khumal 2, Monoprecos, NGRCO9569, NGRCO9571, Srijana) to ensure genetic diversity in the dataset [3].
• Sensor Systems: Visible spectrum (RGB) and Visible/Near-Infrared (Vis/NIR) spectroscopic cameras mounted on UAVs or fixed platforms [49].
• Growth Facilities: Controlled-environment growth chambers or greenhouse bays with programmable irrigation systems.
• Computing Hardware: Workstation with high-performance GPU (e.g., NVIDIA RTX series) for deep learning.

Procedure:

• Plant Cultivation & Stress Induction: Cultivate tomato plants under optimal conditions until the target developmental stage (e.g., 4-6 leaf stage). Divide plants into treatment groups:
  • Control Group (0% PEG): Maintained at optimal soil moisture.
  • Stress Group 1 (3% PEG, -0.18 MPa): Induced mild drought stress using polyethylene glycol (PEG-6000) in the growth medium [3].
  • Stress Group 2 (6% PEG, -0.36 MPa): Induced severe drought stress [3].
  • A completely randomized design (CRD) should be used for plant placement.
• Image Acquisition: Capture high-resolution images of plant canopies daily for the duration of the experiment. Ensure consistent lighting, camera angle, and distance. For each plant, capture both RGB and Vis/NIR spectral data [49].
• Data Annotation: Manually annotate all acquired images, marking bounding boxes around early-stress indicators (e.g., slightly wilted leaves, changed leaf angles). Use a standardized annotation tool (e.g., LabelImg). Annotations should be cross-verified by multiple plant science experts.
• Dataset Curation: Split the annotated dataset into training (70%), validation (15%), and test (15%) sets, ensuring no data from the same plant appears in different splits.

Model Optimization and Training Protocol

Objective: To systematically train and optimize a deep learning model (e.g., a YOLO variant) for small-target detection in the curated dataset.

Procedure:

• Model Selection & Modification: Select a base model (e.g., YOLOv8). Implement the architectural optimizations described in Section 2, which may include:
  • Integrating a super-resolution preprocessing step (e.g., ESRGAN) [47].
  • Replacing the SPPF module in the backbone with an MSFA module [47].
  • Incorporating a Multi-scale Feature Enhancement Module (MFEB) [48].
  • Adding a dynamic detection head (DyHead) [47].
• Hyperparameter Configuration: Set initial learning rate, batch size, and optimizer (e.g., SGD, Adam) based on model specifications. Use the validation set for hyperparameter tuning.
• Model Training: Train the model on the training set. Employ early stopping based on validation loss to prevent overfitting.
• Model Validation: Evaluate the trained model on the held-out test set. Use standard object detection metrics: mean Average Precision (mAP) at IoU thresholds of 0.5 (mAP@0.5) and 0.5:0.95 (mAP@0.5:0.95), precision, and recall [46] [47].

Table 1: Quantitative Performance Metrics for Optimized Detection Models

Model Variant	mAP@0.5 (%)	mAP@0.5:0.95 (%)	Precision (%)	Recall (%)	GFLOPs
Baseline YOLOv8n	94.7	65.1	92.4	89.8	8.2
+ Super-resolution & MSFA [47]	96.4	68.9	95.1	92.3	9.5
+ MFEB & Snake Conv [48]	96.5	69.5	95.8	93.1	9.2

Workflow and System Architecture Visualization

The following diagram illustrates the integrated workflow for data acquisition, model optimization, and drought stress detection.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials and Reagents for Drought Stress Detection Experiments

Item Name	Function / Application	Example Specification / Note
Polyethylene Glycol 6000 (PEG-6000)	Induces osmotic stress in growth medium to mimic drought conditions for controlled phenotyping [3].	Use at concentrations of 3% (-0.18 MPa) for mild stress and 6% (-0.36 MPa) for severe stress [3].
Visible/Near-Infrared (Vis/NIR) Spectrometer	Captures spectral data from leaves; specific bands are highly correlated with early drought stress before visible symptoms appear [49].	Key feature bands identified by models like 1D-ResGC-Net can be used for early, non-destructive detection [49].
Tomato Genotypes (Diverse)	Provides genetic variability essential for identifying drought-resilient traits and validating model generalizability [3].	Include both commercial cultivars (e.g., Monoprecos) and local landraces (e.g., NGRCO9569) [3].
ESRGAN Model	A super-resolution model used to preprocess input images, enhancing the resolution of small targets for the detection network [47].	Preprocessing step to improve input data quality before feeding into detection models like YOLO-MST [47].
Multi-Scale Feature Enhancement Module (MFEB)	A network module that refines focus on low-level features through multi-scale and selection strategies, mitigating background interference [48].	Integrated into the model architecture (e.g., YOLO backbone) to improve feature extraction for small targets [48].
Dynamic Detection Head (DyHead)	The prediction head of a network that uses attention mechanisms to dynamically fuse multi-scale features, improving detection accuracy [47].	Replaces standard prediction heads in architectures like YOLO to better handle scale variation [47].

Application Notes

Multi-Source Data Fusion for Comprehensive Drought Stress Assessment

Integrating data from multiple sensor modalities and plant tissues significantly enhances the accuracy and reliability of drought stress detection in tomatoes. This approach overcomes the limitations of single-source data by capturing complementary physiological information. Hyperspectral imaging (HSI) of both young and mature leaves, when combined with deep learning-based feature extraction, has proven highly effective. Studies demonstrate that fusing spectral data from HSI with image features (texture, morphology) extracted via convolutional neural networks like LeNet-5 achieves superior classification performance compared to using either data type alone [50]. This multi-feature approach, when applied to multiple leaf types, provides a holistic view of plant stress responses, capturing differential pieces of information presented by leaves at various developmental stages [50]. The integration of these heterogeneous data sources enables early detection of drought stress before visible symptoms appear, facilitating timely intervention.

Wearable Sensor Networks for Real-Time Physiological Monitoring

Recent advancements in plant-wearable sensors enable continuous, real-time monitoring of key physiological parameters related to water status. The PlantRing system, a nano-flexible sensing device that measures stem diameter variation (SDV), represents a breakthrough in high-throughput phenotyping [51]. This sensor employs bio-sourced carbonized silk georgette as the strain-sensing material, offering exceptional detection limits (0.03%–0.17% strain), high stretchability (up to 100% tensile strain), and remarkable durability for season-long use [51]. The system operates by transforming mechanical deformations of plant organs into changes in electrical resistance, providing sensitive measurements of diurnal fluctuations in stem circumference that directly reflect plant water status. When deployed across multiple plants simultaneously, these sensors generate high-temporal-resolution data on plant hydraulic responses to drought conditions, enabling quantification of stomatal sensitivity to soil drought—a key trait for drought adaptation that has traditionally been challenging to phenotype [51].

Deep Learning-Enhanced Phenotypic Trait Extraction

Advanced computer vision models, particularly optimized versions of the YOLO (You Only Look Once) architecture, enable automated extraction of structural phenotypic traits from tomato plants under drought stress. Enhanced YOLO models incorporating specialized modules like Adaptive Kernel Convolution (AKConv) and recalibrated feature pyramids demonstrate improved capability for recognizing small target organs and multi-scale features in complex canopy environments [2]. These models facilitate non-destructive measurement of key phenotypic parameters including plant height, petiole count, and leaf number with average relative errors of 6.9% for plant height and 10.12% for petiole count [2]. The bounding box information extracted by these models enables computation of morphological traits through geometric analysis, providing quantitative indicators of drought stress responses. When these phenotypic traits are used as input features for classification algorithms like Random Forest, differentiation of water stress conditions can achieve accuracy up to 98% [2].

Electrochemical Sensing for Early Stress Biomarker Detection

Wearable electrochemical patches enable direct monitoring of stress-induced signaling molecules at the plant surface, providing unprecedented access to early biochemical indicators of drought stress. Recent research demonstrates the development of flexible patch sensors containing micro-needle arrays that attach to the underside of leaves and detect hydrogen peroxide (Hâ‚‚Oâ‚‚), a key distress signal molecule in plants [16]. These patches utilize a chitosan-based hydrogel mixture containing an enzyme that reacts with hydrogen peroxide to produce measurable electrical currents, achieving detection in under one minute at low cost [16]. Validation experiments on soybean and tobacco plants infected with bacterial pathogens confirmed significantly higher electrical currents on stressed leaves compared to healthy controls, with current levels directly correlated with hydrogen peroxide concentrations [16]. This sensing approach provides critical early warning of stress activation before visible symptoms manifest, creating opportunities for pre-symptomatic intervention.

Table 1: Performance Metrics of Data Fusion Strategies for Drought Stress Detection

Strategy	Data Types Fused	Key Performance Metrics	Advantages
Hyperspectral Imaging with Multi-Feature Fusion [50]	Spectral data + Image features (texture, morphology) + Multiple leaf types	95.78% classification accuracy; 95.90% calibration accuracy	Captures both internal composition and external features; Leverages differential leaf responses
Wearable Sensor Networks [51]	Stem diameter variation + Environmental parameters (temperature, humidity)	0.03%-0.17% strain detection limit; 100% stretchability; 10,000 cycle durability	Real-time continuous monitoring; High-throughput capability; Season-long stability
Deep Learning Phenotyping [2]	Visual imagery + Extracted phenotypic traits (plant height, petiole count)	98% classification accuracy; 6.9% error for plant height; 10.12% error for petiole count	Non-destructive measurement; Automated trait extraction; High correlation with stress levels
Electrochemical Sensing [16]	Hydrogen peroxide levels + Spatial distribution	<1 minute detection time; <$1 per test; 9x reusability	Early pre-visual detection; Direct biomarker measurement; Low cost

Table 2: Sensor Technologies for Tomato Drought Stress Detection

Technology	Measured Parameters	Detection Mechanism	Sensitivity	Implementation Scale
Hyperspectral Imaging [50] [52]	Spectral reflectance (400-2500 nm); Pigment dynamics; Water absorption bands	Spatial and spectral data fusion; Spectral signature analysis	High - detects pre-visual stress	Individual plants to field scale
PlantRing Wearable Sensor [51]	Stem diameter variation; Circumference dynamics	Strain sensing; Resistance change measurement	0.03%-0.17% strain detection limit	High-throughput (300+ units simultaneously)
Electrochemical Patch [16]	Hydrogen peroxide concentration	Enzyme-based electrochemical detection; Current measurement	Accurate at nanomolar levels	Individual leaf level
YOLO-based Vision Models [53] [2]	Morphological traits; Plant architecture; Organ counting	Deep learning; Bounding box detection; Feature extraction	71.0% recall; 76.5% mAP@0.5 [53]	Greenhouse and field environments

Experimental Protocols

Protocol: Hyperspectral Imaging with Multi-Feature Fusion

Purpose: To detect drought stress in tomato plants by fusing spectral and image features from young and mature leaves using hyperspectral imaging and subsample fusion.

Materials and Reagents:

• Hyperspectral imaging system (400-1000 nm or 400-2500 nm range)
• Supplemental blue light sources (460-480 nm)
• Tomato plants (Solarium lycopersicum) at different growth stages
• Controlled environment greenhouse with temperature and humidity control
• Soil moisture sensors for validation
• Computer with HSI data processing software (Python with scikit-learn, TensorFlow)

Procedure:

• Plant Preparation and Stress Treatment:
  • Transplant 50-day-old tomato seedlings into individual pots (3 L capacity) with standardized soil.
  • Maintain initial soil relative humidity (SRH) at 60-80% for root establishment.
  • Apply three water stress treatments: well-watered (60-80% SRH), reduced-watered (40-60% SRH), and deficient-watered (20-40% SRH).
  • Monitor SRH daily using calibrated soil moisture sensors.

• Hyperspectral Image Acquisition:

  • Set up HSI system with supplemental blue lights to improve signal-to-noise ratio in visible region.
  • Acquire HSI images of both young and mature leaves from each plant under each stress treatment.
  • Ensure consistent imaging geometry and distance for all measurements.
  • Extract reflectance spectra from regions of interest in young and mature leaves.

• Feature Extraction and Fusion:

  • Screen effective wavelengths (EWs) using genetic algorithm from extracted spectra.
  • Determine reference image using ReliefF algorithm.
  • Select first four reflectance images of EWs with low correlation to reference using Pearson's correlation analysis.
  • Extract image features from reflectance image set (RIS) using LeNet-5 deep learning architecture.
  • Fuse spectral data of EWs with deep learning image features.

• Model Development and Validation:

  • Develop classification models (SVM, Random Forest, DenseNet) using fused features.
  • Implement subsample fusion integrating data from young and mature leaves.
  • Validate models using cross-validation and independent test sets.
  • Evaluate performance based on classification accuracy, precision, recall, and F1-score.

Troubleshooting Tips:

• If image quality is poor, optimize blue light intensity and positioning.
• If classification accuracy is low, adjust feature selection parameters or try alternative fusion strategies.
• Ensure consistent leaf positioning during imaging to minimize variation.

Protocol: Wearable Sensor Deployment for Stem Diameter Monitoring

Purpose: To monitor tomato plant water status in real-time using PlantRing wearable sensors for stem diameter variation (SDV) measurements.

Materials and Reagents:

• PlantRing sensor units (6 cm model for tomato stems)
• Gateway device for wireless communication
• Cloud-based data platform access
• Computer or smartphone for data monitoring
• Automated cable ties for sensor attachment
• Multi-head charger for continuous power supply

Procedure:

• Sensor Calibration:
  • Calibrate each sensor unit before deployment by stretching consistently to create strain-to-AD signal response curve.
  • Apply linear function fitting to establish calibration curve (Supplemental Figure 4 in [51]).
  • Verify sensor performance across expected measurement range.

• Sensor Deployment:

  • Select healthy, representative tomato plants at appropriate growth stage.
  • Attach sensor units to plant stems using built-in U-shaped handles and automated cable ties.
  • Adjust sensor length using flexible clip mechanism to ensure proper fit without constricting growth.
  • Connect sensors to gateway device using 2.4 GHz radio-frequency technology.
  • Verify data transmission to cloud server via 4G/5G networks.

• Data Collection and Monitoring:

  • Set data transmission interval to 1 second for high-temporal-resolution monitoring.
  • Monitor data remotely using computer or smartphone interface.
  • Collect continuous measurements of stem diameter variations over multiple diurnal cycles.
  • Record environmental parameters (temperature, humidity) using integrated sensors for data compensation.

• Data Analysis:

  • Apply temperature compensation using quadratic polynomial regression model.
  • Analyze diurnal patterns in stem diameter variations.
  • Correlate SDV patterns with irrigation events and environmental conditions.
  • Implement threshold-based alerts for significant stress indicators.

Troubleshooting Tips:

• If data transmission is unstable, check gateway distance (max 60 m line-of-sight).
• If sensors loosen over time, readjust cable ties to maintain proper contact.
• For noisy signals, verify sensor calibration and environmental compensation.

Protocol: Deep Learning-Based Phenotypic Trait Extraction

Purpose: To automatically extract key phenotypic traits from tomato plants under water stress using improved YOLOv11n model.

Materials and Reagents:

• Digital camera (RGB) with consistent resolution and lighting
• Tomato plants under controlled water stress conditions
• Computer with GPU capability for deep learning
• Python environment with PyTorch and OpenCV
• Annotation software for image labeling
• Measurement tools for validation (ruler, calipers)

Procedure:

• Image Acquisition:
  • Capture images of tomato plants from multiple angles under consistent lighting conditions.
  • Ensure images include complete plants with clear visibility of leaves, petioles, and stem.
  • Acquire images at regular intervals (e.g., daily) throughout stress treatment period.
  • Maintain consistent camera settings and distance across all imaging sessions.

• Dataset Preparation:

  • Annotate images with bounding boxes for key phenotypic traits: leaves, petioles, and stem apex.
  • Split dataset into training, validation, and test sets (typical ratio: 70:15:15).
  • Apply data augmentation techniques (rotation, scaling, brightness adjustment) to improve model robustness.

• Model Development:

  • Implement improved YOLOv11n architecture with Adaptive Kernel Convolution (AKConv) integrated into C3k2 modules.
  • Design recalibration feature pyramid detection head based on P2 layer for improved small-object detection.
  • Train model using transfer learning approach with pre-trained weights.
  • Optimize hyperparameters (learning rate, batch size) based on validation performance.

• Phenotypic Parameter Computation:

  • Extract bounding box information from trained model predictions.
  • Compute plant height from vertical coordinate difference between lowest and highest bounding boxes.
  • Count petioles and leaves based on detected instances.
  • Calculate additional morphological parameters through geometric analysis of detection results.

• Stress Classification:

  • Use extracted phenotypic traits as input features for classification algorithms (Random Forest, SVM, etc.).
  • Train classifiers to differentiate between well-watered and water-stressed plants.
  • Validate classification accuracy using cross-validation and independent test sets.

Troubleshooting Tips:

• If model detection performance is poor, increase dataset size or adjust augmentation strategies.
• For inaccurate phenotypic measurements, verify annotation quality and calibration.
• If classification accuracy is low, try different feature combinations or classifier algorithms.

Visualization Diagrams

The Scientist's Toolkit

Table 3: Essential Research Reagent Solutions and Materials

Item	Function/Application	Specifications/Notes
Hyperspectral Imaging System [50] [52]	Captures spatial and spectral data for stress indicator detection	400-2500 nm range; Supplemental blue lights (460-480 nm) for improved SNR
PlantRing Wearable Sensor [51]	Monitors stem diameter variation as water status indicator	Carbonized silk georgette strain sensor; 0.03%-0.17% detection limit; 100% stretchability
Electrochemical Patch [16]	Detects hydrogen peroxide stress biomarkers	Chitosan-based hydrogel with enzyme; <1 minute detection; <$1 per test
YOLOv11n Deep Learning Model [53] [2]	Automated phenotypic trait extraction	With AKConv and recalibrated feature pyramid; 71.0% recall, 76.5% mAP@0.5
Controlled Environment Greenhouse [2] [50]	Maintains specific growth conditions	Temperature control (24°C); Humidity control (68%); Artificial supplementary lighting
Soil Moisture Sensors [2] [50]	Validates irrigation treatments and soil water status	Monitors soil relative humidity (SRH) across 20-80% range
Python ML Libraries (scikit-learn, TensorFlow, PyTorch) [2] [50]	Data analysis and model development	For classification algorithms (SVM, Random Forest) and deep learning implementations

Balancing Sensor Sensitivity, Durability, and High-Throughput Requirements

The implementation of robust sensor systems for the early detection of drought stress in tomatoes is critical for advancing precision agriculture and mitigating yield loss. Effective systems must balance three competing demands: high sensitivity to capture subtle physiological changes, exceptional durability to withstand long-term field deployment, and high-throughput capability to screen large plant populations efficiently [34] [10]. This balance is non-trivial; for instance, highly sensitive micro-structured sensors may lack the durability required for season-long use, while rugged sensors often lack the sensitivity for early stress detection [54] [10]. These application notes provide a structured framework and detailed protocols for researchers aiming to optimize this tripartite balance in tomato drought stress research, enabling a shift from experience-based to data-driven irrigation management.

Performance Trade-off Analysis of Sensing Modalities

A systematic analysis of sensor performance, based on a structured review of recent literature, reveals clear trade-offs between sensitivity, durability, and integration potential. The following table summarizes the quantitative performance of different sensor classes relevant to plant phenotyping.

Table 1: Performance Comparison of Sensing Modalities for Drought Stress Detection

Sensing Modality	Key Measurands	Sensitivity / Performance	Durability (Cycles or Duration)	Throughput Potential	Key Trade-offs
Capacitive Pressure Sensors (CPS) - Microstructuring [54]	Pressure (e.g., sap pressure, tactile sensing)	0.55 - 14.27 kPa⁻¹	>10,000 cycles (varies by design)	Medium	Balances sensitivity and integration well; fabrication complexity can limit scalability.
Wearable Strain Sensors (PlantRing) [10]	Stem diameter variation (growth, water status)	Detection limit: 0.03–0.17% strain	Season-long use	High	Excellent durability and throughput; requires adaptation to different plant morphologies.
Acoustic Emission Sensors [34]	Xylem cavitation sounds	Clear indicator of early drought stress	Information Missing	Low	Highly sensitive to early stress; potentially lower throughput due to data complexity.
Stomatal Conductance/Pore Area Sensors [34]	Stomatal dynamics	Reacts within 24h of water withholding	Information Missing	Low	Direct physiological measurement; often contact-based and lower speed.
Deep Learning-based Image Analysis [2]	Plant height, petiole count, canopy structure	98% classification accuracy for water stress; 6.9% avg. error for plant height	Software-based (inherently high)	Very High	Non-contact, highly scalable; requires model training and computational resources.

Experimental Protocols for Sensor Validation and Deployment

Protocol: Validation of Wearable Stem Diameter Sensors

This protocol outlines the procedure for deploying and validating a high-throughput wearable sensor system, such as the PlantRing, for monitoring tomato water relations [10].

• Objective: To reliably monitor plant growth and water status in real-time through organ circumference dynamics under controlled and field conditions.
• Materials:
  • PlantRing sensors or equivalent flexible strain sensors (using materials like bio-sourced carbonized silk georgette) [10].
  • Data acquisition unit (e.g., wireless node for high-throughput settings).
  • Mature, high-wire tomato plants grown in a controlled substrate (e.g., rockwool, soil).
  • Precision scale (for pot weight-based transpiration measurement).
  • Control and treatment irrigation systems.
• Procedure:
  • Sensor Calibration: Calibrate each sensor against known mechanical strains (e.g., 0.03% to 100% tensile strain) to establish a baseline strain-resistance relationship [10].
  • Field Deployment: Fit the sensors gently around the main stem or fruit peduncle of selected tomato plants. Ensure firm but non-constructing contact to avoid damaging the plant tissue.
  • Experimental Setup: Divide plants into two groups: a well-watered control and a drought-stress treatment. For the treatment group, withhold irrigation completely for 2-3 days to induce rapid but reversible stress [34].
  • Data Collection:
    • Record stem diameter variations from the sensors at 5-15 minute intervals.
    • Simultaneously, record whole-plant transpiration rates via daily pot weighing.
    • Monitor environmental parameters (e.g., VPD, light intensity) to correlate with diurnal stem expansion and contraction.
  • Data Analysis: Analyze the data for key metrics: stem growth rate, maximum daily shrinkage (MDS), and stem diameter variation (SDV). Correlate these metrics with soil water content and transpiration data to identify early stress signatures.

Protocol: Image-Based Phenotyping for High-Throughput Drought Screening

This protocol describes a deep learning-based workflow for high-throughput phenotypic trait extraction from tomato plants under varying water stress [2].

• Objective: To automatically extract key phenotypic traits from tomato images for early identification of water stress using an improved YOLOv11n model.
• Materials:
  • Imaging setup: An aluminum alloy frame with consistent lighting (e.g., T8 LED tubes) and a high-resolution RGB camera [2].
  • Potted tomato plants at a consistent growth stage.
  • Computer with GPU and deep learning software environment (e.g., Python, PyTorch).
• Procedure:
  • Image Acquisition: Place individual potted tomato plants within the imaging frame. Capture standardized images of each plant from a fixed angle and distance daily throughout the water stress experiment.
  • Model Training:
    • Dataset Preparation: Annotate acquired images, labeling key structures like leaves, petioles, and overall plant height.
    • Model Customization: Implement an improved YOLOv11n architecture by integrating Adaptive Kernel Convolution (AKConv) and a recalibrated feature pyramid network to enhance small-object detection [2].
    • Training: Train the model on the annotated dataset to detect and localize the defined phenotypic traits.
  • Trait Extraction and Analysis:
    • Use the trained model to process new images and output bounding boxes for plant structures.
    • Calculate key phenotypic parameters from the bounding box information via geometric analysis (e.g., plant height from the bounding box height, petiole count from the number of detected petioles).
    • Construct weighted combinations of these traits (e.g., plant height, leaf count) and use classification algorithms like Random Forest to differentiate between well-watered and stressed plants [2].

Visualization of Experimental Workflows

Sensor Integration and Data Analysis Workflow

The following diagram illustrates the logical flow for integrating multi-modal sensor data for drought stress detection and irrigation control.

Title: Sensor-integrated drought stress detection workflow

High-Throughput Phenotyping Protocol

This diagram outlines the specific steps for the high-throughput, image-based phenotyping protocol.

Title: High-throughput image-based phenotyping protocol

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 2: Key Research Reagent Solutions for Sensor-Based Drought Stress Research

Item	Function / Application	Key Characteristics	Research Context
Flexible Capacitive Pressure Sensors [54]	Measuring subtle pressure changes (e.g., sap flow, turgor).	Micro-structured dielectrics (e.g., pyramids); High sensitivity (up to 14.27 kPa⁻¹).	Used in developing wearable "electronic skins" for plants; requires encapsulation for field use.
PlantRing / Nano-Flexible Strain Sensor [10]	Monitoring stem/fruit circumference dynamics (growth, water status).	Carbonized silk georgette; High stretchability (up to 100%), durable for season-long use.	Enables high-throughput quantification of stomatal sensitivity to soil drought and feedback irrigation.
Polyimide (PI) [55]	Protective coating and substrate for micro-sensors (e.g., humidity, temperature).	Excellent thermal stability, flexibility, and dielectric properties.	Critical for enhancing the durability and reliability of MEMS-based sensors in harsh greenhouse/field environments.
Bio-sourced Carbonized Silk [10]	Conductive, flexible sensing material for strain gauges.	Biocompatible, high conductivity, mechanically robust.	A sustainable material choice for creating durable, high-sensitivity wearable plant sensors.
Gold (Au) Thin Films [55]	Sensing electrodes in MEMS-based micro-sensors.	Excellent conductivity, chemical inertness, and straightforward fabrication.	Used in reliable, self-fabricated micro-sensors for temperature, humidity, and velocity.
YOLOv11n Deep Learning Model [2]	Automated, high-throughput extraction of phenotypic traits from plant images.	Integrated with AKConv; high mAP50 for small object detection.	Provides a non-contact, scalable method for screening tomato plants for early water stress responses.

Validating Sensor Efficacy: Genotype Screening and System Performance

Controlled Drought Stress Validation Using PEG-Induced Osmotic Stress

Implementing sensor systems for the early detection of drought stress in tomato research requires robust, controlled validation methods. Polyethylene glycol (PEG)-induced osmotic stress serves as a reliable, reproducible laboratory technique to simulate soil moisture deficit, enabling the calibration and verification of sensor outputs against known physiological responses. This protocol details the application of PEG-6000 for drought stress validation, bridging traditional phytophysiology with modern sensor technology.

Theoretical Framework and Key Signaling Pathways

PEG-6000, a high-molecular-weight, inert polymer, lowers the water potential of growth media without permeating plant cell walls, effectively mimicking the physiological drought conditions encountered in field environments. The ensuing osmotic stress triggers a defined cascade of biochemical and physiological responses in tomato plants, which can be correlated with sensor data for system calibration [23] [56].

The diagram below illustrates the primary signaling pathway activated by PEG-induced osmotic stress and the key measurable physiological parameters that can be monitored by sensor systems.

Figure 1: PEG-Induced Osmotic Stress Signaling Pathway. This diagram outlines the key physiological and biochemical responses in tomatoes under PEG-induced drought stress. ABA, Abscisic Acid; ROS, Reactive Oxygen Species; SOD, Superoxide Dismutase; CAT, Catalase; APX, Ascorbate Peroxidase.

Research Reagent Solutions and Essential Materials

The following table catalogs the critical reagents and materials required for implementing PEG-induced osmotic stress protocols and validating associated sensor systems.

Table 1: Essential Research Reagents and Materials for PEG-Induced Drought Stress Experiments

Item	Function/Application	Key Specifications
PEG-6000	Induces controlled osmotic stress in growth media; used for both stress imposition and seed priming [23] [56].	Molecular weight ~6000; Osmotic potential: -0.18 MPa (3%), -0.36 MPa (6%) [23].
Hydrogen Peroxide (Hâ‚‚Oâ‚‚) Sensor	Directly measures key oxidative stress signaling molecule in plant tissue; validates plant stress status [16].	Wearable patch sensor; provides real-time, in-planta measurements; <1 minute response time [16].
Deep Learning Model (YOLOv11n)	Automated, high-throughput analysis of phenotypic traits from plant images under stress conditions [2].	Enables non-destructive measurement of plant height, petiole count, leaf number; ~98% classification accuracy for water stress [2].
Antioxidant Assay Kits	Quantify enzyme activity (SOD, POD, CAT, APX) to assess oxidative stress response and mitigation [56].	Spectrophotometric assays; activities can increase by 30-45% in primed plants under stress [56].

Experimental Protocols and Workflows

Protocol 1: Seed Germination and Early Seedling Screening

This protocol is designed for high-throughput screening of tomato genotypes for early-stage drought tolerance, providing baseline data for sensor system development [23] [57].

Workflow Overview:

Figure 2: Seed Germination and Screening Workflow. CRD: Completely Randomized Design; MGIDI: Multi-Trait Genotype-Ideotype Distance Index; DRI: Drought Resistance Index.

Detailed Methodology:

• Seed Preparation: Surface sterilize tomato seeds (e.g., genotypes NGRCO9569, Khumal 2, Srijana) using 70% ethanol for 1 minute, followed by 2% sodium hypochlorite for 10 minutes, and rinse thoroughly with sterile distilled water [23].
• PEG Solution Preparation: Prepare aqueous solutions of PEG-6000 at concentrations of 0% (control), 3% (-0.18 MPa), and 6% (-0.36 MPa) in a standardized nutrient solution such as Hoagland's. Confirm the water potential of each solution using a vapor pressure osmometer [23].
• Germination Setup: Place sterilized seeds in Petri dishes (e.g., 20 per dish) lined with filter paper. Add 10 mL of the respective PEG solution to each dish. Arrange dishes in a Completely Randomized Design (CRD) within a growth chamber set to 25°C, 16/8 hour light/dark cycle, and 70% relative humidity [23] [57].
• Data Collection:
  • Germination Percentage: Record daily until no further germination occurs for 48 hours. A seed is considered germinated upon radicle emergence (≥2 mm). Calculate final germination percentage (GP) [23].
  • Germination Rate: Compute using the formula: Germination Rate = Σ(Number of seeds germinated on day i) / i for all days of the germination period [23].
  • Seedling Traits: After 14 days, measure seedling shoot length (SL) and root length (RL). Separately harvest and oven-dry (70°C for 48 hours) shoots and roots to determine dry biomass. Calculate the Vigor Index (VI) = GP × (SL + RL) and Root-to-Shoot Ratio (R/S) based on dry weight [23].

Protocol 2: Seed Priming for Enhanced Stress Tolerance

Seed priming with PEG pre-conditions plants to better withstand subsequent stress, a key interaction point for evaluating sensor performance during stress recovery [56].

Detailed Methodology:

• Priming Solution: Prepare a PEG-6000 solution with a water potential of -1.2 MPa [56].
• Priming Process: Immerse tomato seeds (e.g., 'Micro-Tom') in the PEG solution for 24 hours at 25°C in darkness.
• Post-Priming Handling: After priming, thoroughly rinse the seeds with sterile distilled water and surface-dry them back to their original moisture content before proceeding with sowing or further stress treatments [56].

Protocol 3: Sensor Integration and Phenotypic Data Acquisition

This protocol leverages deep learning for high-throughput phenotyping, creating a bridge between sensor data and actionable physiological insights [2].

Workflow Overview:

Figure 3: Sensor and Phenotyping Integration Workflow.

Detailed Methodology:

• Image Acquisition: Grow tomato plants under controlled conditions and impose water stress. Capture high-resolution RGB images of plants against a neutral background at regular intervals [2].
• Phenotypic Trait Extraction: Process images using an improved YOLOv11n deep learning model. The model automatically identifies and computes key structural traits, including plant height, leaf count, and petiole count, from bounding box information [2].
• Real-Time Stress Signal Detection: Attach a wearable hydrogen peroxide sensor patch to the abaxial side of a fully expanded leaf. For the electrochemical patch, record the electrical current generated, which is directly proportional to the local concentration of Hâ‚‚Oâ‚‚ in the leaf apoplast [16].
• Data Integration and Model Training: Use the extracted phenotypic traits and Hâ‚‚Oâ‚‚ sensor data as input features. Train machine learning classification algorithms (e.g., Random Forest, Support Vector Machine) to differentiate between varying levels of water stress with high accuracy [2].

Quantitative Data Analysis and Presentation

Germination and Seedling Vigor Response

The following table summarizes expected quantitative outcomes from the germination and early seedling screening protocol (Protocol 1), providing a benchmark for sensor validation [23] [57].

Table 2: Quantitative Germination and Seedling Responses of Tomato Genotypes under PEG-Induced Drought Stress

Genotype	PEG Concentration	Germination Percentage (%)	Germination Rate	Vigor Index	Root-to-Shoot Ratio (Dry Weight)
NGRCO9569	0%	~100	~4.34	~High	~Baseline
	3%	~100 (Maintained)	Maintained	~High (Maintained)	> Baseline
	6%	~100 (Maintained)	Maintained	~High (Maintained)	>> Baseline
Khumal 2	0%	~93.33	~3.52	~High	~Baseline
	3%	~93.33 (Maintained)	Maintained	~High (Maintained)	> Baseline
	6%	~73.33 (Reduced)	Reduced	Reduced	>> Baseline
Srijana (Susceptible)	0%	100	~1.69	High	~Baseline
	3%	13.33 (Sharp Decline)	Very Low	Very Low	Not Significant
	6%	0 (Complete Failure)	0	0	Not Applicable

Physiological and Biochemical Responses

This table consolidates data on the physiological effects of drought stress and the mitigating impact of seed priming, representing key endpoints for sensor correlation [56].

Table 3: Physiological and Biochemical Changes in Tomato under Stress and with PEG Priming

Parameter	Change under 150 mM NaCl Stress (vs. Control)	Effect of PEG-6000 Seed Priming (under 150 mM NaCl Stress)	Relevance to Sensor Development
Leaf Na+ Content	+66% (in non-primed)	-28% (vs. non-primed under stress) [56]	Indicator of ion homeostasis; target for ionic sensors.
Leaf K+ Content	-41% (in non-primed)	+42% (vs. non-primed under stress) [56]	Indicator of ion homeostasis; critical for cellular function.
Hâ‚‚Oâ‚‚ (in planta)	Significant Increase [16]	Not Reported (Presumably Reduced)	Directly measured by Hâ‚‚Oâ‚‚ patch sensor; key early stress signal [16].
MDA (Oxidative Damage)	Significant Increase	-35% (vs. non-primed under stress) [56]	Correlate for non-destructive oxidative stress sensors.
Antioxidant Enzymes (SOD, CAT, APX)	Variable/Increased	+30% to +45% (vs. non-primed under stress) [56]	Biochemical validation of plant defense activation.
Plant Height	Decrease	Not Reported (Likely Mitigated)	Directly measured by image-based phenotyping [2].
Relative Water Content	Decrease	+18% (vs. non-primed under stress) [56]	Key hydration metric; target for spectral/thermal sensors.

Comparative Performance of Tomato Genotypes for Drought Tolerance Traits

Drought stress is a major abiotic constraint to tomato (Solanum lycopersicum L.) production, particularly at early developmental stages, and can cause yield losses of up to 50% [3] [9]. Changing climatic conditions and the declining availability of water for agriculture necessitate the urgent development of drought-tolerant varieties [3]. This application note provides a structured framework for evaluating tomato genotype responses to water deficit, integrating traditional phenotypic screening with advanced sensor technologies for drought stress detection. The protocols outlined support the identification of resilient genotypes and the elucidation of underlying tolerance mechanisms, contributing to the development of climate-resilient cultivars suited for low-irrigation environments.

Performance Evaluation of Tomato Genotypes Under Drought Stress

Germination and Early Seedling Stage Screening

Controlled screening at the germination stage provides an efficient method for early selection of drought-tolerant genotypes. A study evaluated five tomato genotypes under polyethylene glycol (PEG)-induced drought stress, revealing significant genotype × stress interactions (p < 0.05) across germination and seedling traits [3].

Table 1: Germination performance of tomato genotypes under PEG-induced drought stress [3]

Genotype	Germination % (Control)	Germination % (3% PEG)	Germination % (6% PEG)	Germination Rate (Ratio)
NGRCO9569	~93%	~93%	~87%	4.34
Monoprecos	~87%	~93%	~73%	3.22
Khumal 2	~93%	~93%	~73%	3.52
NGRCO9571	~100%	~60%	~47%	1.92
Srijana	~100%	~13%	~0%	1.69

NGRCO9569, Monoprecos, and Khumal 2 maintained high germination rates, seedling vigor, and biomass under stress, with NGRCO9569 showing no significant germination reduction even at 6% PEG [3]. In contrast, Srijana exhibited sharp declines in all measured parameters, including complete germination failure at 6% PEG [3]. Multivariate analyses, including MGIDI and DRI indices, consistently ranked NGRCO9569 and Khumal 2 as top performers [3].

Vegetative and Reproductive Stage Evaluation

Drought tolerance at advanced growth stages is crucial for yield stability. A study of 126 F6 tomato lines and five parental varieties under irrigated and drought environments identified twelve superior genotypes using stress tolerance index analysis [22]. The selected lines demonstrated optimal performance across multiple parameters under water-limited conditions, providing valuable genetic material for breeding programs [22].

Table 2: Drought tolerance indices and their applications in tomato screening

Tolerance Index	Application in Tomato Drought Screening	Key Findings
Stress Tolerance Index (STI)	Selection of high-yielding genotypes under both stress and non-stress conditions [22]	Identified 12 superior F6 tomato lines with adaptive traits [22]
Multi-Trait Genotype-Ideotype Distance Index (MGIDI)	Multi-trait selection for drought resilience [3]	Ranked NGRCO9569 and Khumal 2 as top performers [3]
Drought Resistance Index (DRI)	Quantification of genotypic responses under drought [3]	Consistently identified tolerant genotypes in PEG screening [3]

Experimental Protocols for Drought Stress Phenotyping

Protocol 1: PEG-Mediated In Vitro Screening of Tomato Genotypes

Principle: Polyethylene glycol (PEG-6000) mimics drought stress by lowering the water potential of the growth medium, enabling controlled, reproducible screening of germination and early seedling traits without toxic side effects [3].

Materials:

• Tomato seeds of test genotypes
• PEG-6000
• Growth chambers or controlled environment facilities
• Germination paper or sterile petri dishes
• Measuring tools (vernier calipers, rulers, digital balance)

Procedure:

• Solution Preparation: Prepare aqueous solutions of PEG-6000 at concentrations of 0% (control), 3% (-0.18 MPa), and 6% (-0.36 MPa) in distilled water [3].
• Experimental Design: Arrange treatments in a completely randomized design (CRD) with three replications [3].
• Seed Germination: Surface-sterilize tomato seeds and place on germination paper saturated with respective PEG solutions [3].
• Data Collection (Day 5-14):
  • Record germination percentage daily [3]
  • Calculate germination rate using standard formulas [3]
  • Measure root and shoot lengths of seedlings [3]
  • Determine seedling fresh and dry weights [3]
  • Calculate seedling vigor index and root-to-shoot ratio [3]
• Statistical Analysis: Perform analysis of variance (ANOVA) for genotype, treatment, and interaction effects. Apply multivariate analyses (MGIDI) and calculate drought tolerance indices (DRI) to rank genotypes [3].

Protocol 2: Sensor-Based Drought Stress Monitoring in Tomato

Principle: In vivo sensors enable real-time monitoring of plant physiological status by detecting changes in sap ion composition under drought stress [8] [9].

Materials:

• Bioristor sensors (Organic ElectroChemical Transistors)
• Tomato plants at vegetative stage
• Data acquisition system
• Potting system with soil moisture sensors
• Environmental monitoring equipment (temperature, humidity sensors)

Procedure:

• Sensor Installation: Implant bioristor sensors into the stem of tomato plants, ensuring contact with the vascular tissue [8] [9].
• Experimental Setup: Establish different irrigation regimes (e.g., 100%, 70%, 50% of crop evapotranspiration) [9].
• Continuous Monitoring: Record sensor response (R) continuously throughout the growth cycle [9].
• Calibration: Correlate sensor output with environmental parameters (vapor pressure deficit, soil moisture) and physiological measurements (leaf water potential, stomatal conductance) [8].
• Data Analysis: Analyze sensor response patterns to identify early stress signatures and quantify stress intensity [8] [9].

Table 3: Advanced sensor technologies for drought stress detection in tomato

Sensor Technology	Measured Parameters	Detection Capability	Implementation Context
Bioristor (OECT-based)	Ion concentration in sap [8] [9]	Real-time detection of drought stress immediately after defence response priming [8]	Field conditions, continuous monitoring [9]
Hyperspectral Imaging	Leaf reflectance spectra [41]	Classification accuracy of 95.9% for drought stress levels [41]	Controlled environment, high-throughput phenotyping [41]
Terahertz Spectroscopy	Leaf water content [43]	Correlation coefficient of 0.979 with actual moisture content [43]	Laboratory conditions, non-destructive leaf sampling [43]
FPGA-Based Smart Sensor	Photosynthesis, transpiration dynamics [58]	Early detection of stomatal closure responses [58]	Greenhouse, physiological process monitoring [58]

Integration of Sensor Systems with Traditional Phenotyping

The combination of sensor technologies with traditional drought tolerance indices provides a comprehensive framework for genotype evaluation. Bioristor systems have demonstrated the potential to reduce water supply by 36% or more while maintaining plant productivity [9]. This approach enables researchers to link physiological responses detected by sensors with agronomic performance under drought stress.

The Scientist's Toolkit: Research Reagent Solutions

Table 4: Essential research reagents and materials for tomato drought stress studies

Reagent/Material	Function in Drought Research	Application Example	Considerations
Polyethylene Glycol (PEG-6000)	Osmotic stress induction to simulate drought [3]	In vitro screening of germination and seedling traits [3]	Use high-purity PEG; prepare fresh solutions; concentration-dependent water potential effects
Salicylic Acid (SA)	Exogenous elicitor to enhance antioxidant defense [59]	Foliar application (100-250 mg/L) to mitigate drought effects [59]	Optimal concentration varies by genotype; apply prior to stress induction; include surfactant (0.02% Tween 20)
Nutrient Solution (Yamazaki formula)	Maintain nutrient balance under stress conditions [43]	Soilless cultivation systems for controlled stress studies [43]	Adjust composition based on growth stage; monitor pH and EC regularly
Antioxidant Assay Kits (SOD, APX, CAT)	Quantify oxidative stress response [59]	Evaluation of ROS scavenging capacity in stressed tissues [59]	Collect fresh tissue samples; process immediately or flash-freeze in liquid Nâ‚‚

The comparative performance assessment of tomato genotypes for drought tolerance traits requires an integrated approach combining traditional phenotyping with advanced sensor technologies. The protocols outlined in this application note provide a standardized framework for identifying drought-resilient tomato genotypes, with NGRCO9569, Khumal 2, and select F6 lines demonstrating superior performance under water deficit conditions. The incorporation of sensor systems enables real-time monitoring of plant physiological status, enhancing the efficiency and precision of drought tolerance screening. These methodologies support the development of climate-resilient tomato cultivars through targeted breeding programs, addressing the critical challenge of water scarcity in agricultural production.

Sensor-Based Phenotyping vs. Traditional Physiological Measurements

Drought stress is a major abiotic constraint on tomato production, leading to significant yield and quality losses. Traditional methods for detecting plant water status, while valuable, are often destructive, low-throughput, and ill-suited for real-time monitoring. This Application Note details the methodologies, performance, and implementation of emerging sensor-based phenotyping platforms and contrasts them with established physiological measurements. Framed within a thesis on implementing sensor systems for drought stress detection in tomato research, this document provides structured protocols and quantitative comparisons to guide researchers in selecting and applying these technologies for precise irrigation management and drought resilience breeding.

Comparative Analysis of Phenotyping Approaches

The following table summarizes the key characteristics, advantages, and limitations of sensor-based and traditional phenotyping methods for tomato drought stress.

Table 1: Comparison of Sensor-Based Phenotyping and Traditional Physiological Measurements for Tomato Drought Stress

Feature	Sensor-Based Phenotyping	Traditional Physiological Measurements
Primary Technologies	In vivo biosensors (e.g., Bioristor), Hyperspectral Imaging, Terahertz Spectroscopy, Deep Learning (YOLO models), Wearable sensors (e.g., PlantRing) [8] [2] [10]	Gas Exchange Analysis, Leaf Water Potential, Gravimetric Soil Moisture, Germination Rate Assays, Manual Morphometric Measurements [3] [60]
Throughput	High-throughput, enabling continuous, real-time monitoring of plant populations [8] [10]	Low-throughput, involving point-in-time, destructive, or labor-intensive single-plant measurements [3] [60]
Key Measured Parameters	Stem ion concentration (Bioristor), canopy spectral indices, leaf water content via terahertz waves, structural traits (plant height, petiole count) from imagery [8] [2] [43]	Photosynthetic rate (Pn), Stomatal Conductance (gs), Leaf Water Potential, Germination Percentage, Seedling Biomass [3] [60]
Temporal Resolution	Continuous or near-continuous, allowing for early stress detection immediately after defence response priming [8]	Discrete, intermittent; may miss rapid physiological changes and early stress onset [60]
Level of Automation	Highly automated, from data acquisition to analysis using machine learning (e.g., Random Forest, SVM, DNN) [2] [60]	Primarily manual, requiring significant operator skill and time, leading to potential subjective bias [37] [3]
Quantitative Performance	YOLOv11n: 98% classification accuracy for water stress; mAP50-95 improved by 5.4%; Plant height error: 6.9% [2]	PEG-induced stress screening: Germination rate varies significantly (e.g., from 4.19 in control to 2.16 under stress) [3]
Primary Advantages	Non-destructive, high temporal resolution, provides direct measurement of plant physiological status, suitable for large-scale screening [8] [2] [10]	Well-established, direct measurement of fundamental physiological processes, often considered a "ground truth" [3] [60]
Key Limitations	High initial setup cost, requires technical expertise for data processing and model training, sensitivity to environmental noise in field settings [2] [43]	Destructive or invasive, low throughput, provides only a snapshot in time, not scalable for precision irrigation or large breeding programs [3] [60]

Detailed Experimental Protocols

Protocol 1: Sensor-Based Drought Stress Detection Using Deep Learning and Imagery

This protocol outlines the procedure for using an improved YOLOv11n deep learning model to automatically extract phenotypic traits from tomato plants under water stress for early classification [2] [35].

Materials and Reagents

• Tomato Plants: Cultivar 'Honghongdou' at the four-leaf stage [35].
• Growth Facility: Greenhouse with controlled conditions.
• Imaging Setup: An aluminum alloy frame (120 cm L × 60 cm W × 115 cm H) equipped with three T8 LED tubes for consistent, supplementary lighting [35].
• Image Acquisition System: Standard RGB camera (e.g., Canon EOS 20D with 50 mm lens) [61].
• Computing Hardware: Computer with a GPU capable of running deep learning frameworks (e.g., NVIDIA GPU with CUDA support).
• Software: Python programming environment with PyTorch or PaddlePaddle, OpenCV library, and scikit-learn for machine learning [2] [61].

Experimental Procedure

• Plant Cultivation and Stress Treatment:

  • Transplant tomato seedlings into pots filled with prepared soil at a bulk density of 1.35 g/cm³ and a field capacity of 21.77% [35].
  • Apply varying water stress treatments (e.g., well-watered, mild stress, severe stress) according to a predefined experimental design. Manage plants following standard practices like the Organic Food Tomato Facility Production Technical Specifications [35].

• Image Data Acquisition:

  • Position the camera on the fixed frame to capture consistent top-view or side-view images of the potted plants.
  • Acquire images of each plant at regular intervals (e.g., daily) throughout the growth cycle under consistent lighting conditions provided by the LED setup [2] [35].

• Model Training and Phenotype Extraction:

  • Model Architecture: Utilize the YOLOv11n model, integrating Adaptive Kernel Convolution (AKConv) into the backbone's C3k2 module and designing a recalibration feature pyramid detection head based on the P2 layer to enhance small object detection [2].
  • Training: Train the model on a labeled dataset of tomato images to detect and identify key structural traits such as leaves, petioles, and the overall plant bounding box [2] [37].
  • Trait Calculation: Use the bounding box information output by the trained model to compute phenotypic parameters:
    • Plant Height: Estimated from the vertical dimension of the bounding box [2].
    • Petiole and Leaf Count: Directly counted from the number of detected objects [2].

• Stress Classification:

  • Construct input features from the extracted traits (e.g., plant height, petiole count) and their weighted combinations.
  • Train a Random Forest classifier, which has demonstrated superior performance (up to 98% accuracy), to differentiate between the different water stress conditions based on the phenotypic features [2].

Data Analysis

• Evaluate the object detection model's performance using metrics such as recall, mean Average Precision at IoU=50% (mAP50), and mAP50-95 [2].
• Validate the accuracy of extracted phenotypic parameters by comparing them to manual measurements, calculating average relative errors (e.g., 6.9% for plant height, 10.12% for petiole count) [2].
• Assess the classification model's performance using accuracy, precision, recall, and F1-score on a held-out test set [2].

The following workflow diagram illustrates the complete experimental procedure for deep learning-based phenotyping.

Protocol 2: In Vivo Biosensing for Continuous Plant Status Monitoring

This protocol describes the integration and use of a Bioristor (Organic Electrochemical Transistor) sensor for the continuous monitoring of ion concentration changes in the sap of tomato plants, enabling immediate detection of drought stress onset [8].

Materials and Reagents

• Bioristor Sensor: An in vivo OECT (organic electrochemical transistor) sensor [8].
• Tomato Plants: Various genotypes can be used, depending on the experimental goal.
• Data Logging System: A device to continuously record the electrical signal from the Bioristor.
• High-Throughput Phenotyping Platform (Optional): For correlating sensor data with other physiological and morphological traits [8].

Experimental Procedure

• Sensor Integration:

  • Carefully integrate the Bioristor sensor directly into the stem of the tomato plant. This allows the device to be in contact with the plant's apoplastic fluid and transpiration stream [8].

• Data Collection:

  • Continuously monitor the plant's physiological status by recording the sensor's signal throughout the plant's life cycle.
  • Initiate a controlled drought stress event by withholding irrigation.
  • Simultaneously, collect data from other phenotyping platforms (e.g., imaging) to obtain complementary data on plant defense mechanisms [8].

• Data Analysis:

  • Correlate shifts in the sensor's signal output with the onset of the irrigation deficit.
  • The Bioristor detects changes in the composition and concentration of ions dissolved in the sap, which are among the earliest plant responses to drought stress [8].

Protocol 3: Traditional Physiological Assessment Using PEG-Induced Drought

This protocol employs Polyethylene Glycol (PEG) to simulate drought stress in a controlled laboratory environment, screening tomato genotypes for tolerance based on germination and seedling traits [3].

Materials and Reagents

• Tomato Genotypes: A set of genotypes to be screened (e.g., Khumal 2, Monoprecos, NGRCO9569, NGRCO9571, Srijana) [3].
• Polyethylene Glycol 6000 (PEG-6000): For creating osmotic stress in the growth medium [3].
• Growth Chambers or Incubators: To maintain constant environmental conditions.
• Petri Dishes or Growth Pots.
• Standard Laboratory Equipment: For measuring seedling traits (e.g., ruler, balance).

Experimental Procedure

• Solution Preparation:

  • Prepare aqueous solutions with different PEG-6000 concentrations (e.g., 0% as control, 3%, and 6% w/v) to create varying water potential levels (-0.18 MPa and -0.36 MPa, respectively) [3].

• Germination Assay:

  • Surface sterilize tomato seeds and place them on Petri dishes or in pots containing the prepared PEG solutions or control medium.
  • Arrange the experiment in a Completely Randomized Design (CRD) with sufficient replication.
  • Place the setups in a growth chamber with controlled light and temperature [3].

• Data Collection:

  • Germination Percentage: Record the number of seeds germinated in each treatment over time.
  • Germination Rate: Calculate the speed of germination.
  • Seedling Traits: After a set period, measure morphological parameters such as plant height, root length, and fresh biomass (shoot and root). Calculate derived indices like the Vigor Index and Root-to-Shoot Ratio [3].

• Statistical Analysis and Genotype Ranking:

  • Perform Analysis of Variance (ANOVA) to identify significant effects of genotype, treatment, and their interaction.
  • Use multivariate analyses and drought tolerance indices (e.g., Multi-Trait Genotype-Ideotype Distance Index (MGIDI), Drought Resistance Index (DRI)) to rank genotypes based on their overall performance under stress [3].

Visualization of Research Pathways and Workflows

The following diagram illustrates the logical decision-making process for selecting the most appropriate phenotyping method based on research objectives, scale, and resources.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 2: Key Research Reagent Solutions for Tomato Drought Stress Phenotyping

Item Name	Function/Application	Key Characteristics
Polyethylene Glycol 6000 (PEG-6000)	Simulates drought stress in controlled laboratory conditions by inducing osmotic stress, reducing water potential in the growth medium [3].	Inert, non-penetrating, allows for precise control of water potential; effective for screening germination and early seedling-stage drought tolerance [3].
Bioristor (OECT Sensor)	An in vivo, wearable sensor integrated into the plant stem for continuous monitoring of changes in ion concentration in the sap, enabling immediate detection of drought stress onset [8].	Provides real-time data on plant physiological status; highly sensitive to early defence responses before visible symptoms appear [8].
YOLOv11n Deep Learning Model	An object detection model used for automatic, high-throughput identification and quantification of key tomato phenotypic traits (e.g., plant height, petiole count) from images [2].	Improved with AKConv and a P2 feature pyramid for enhanced small object detection; achieves high accuracy (98%) in classifying water stress levels [2].
Terahertz Time-Domain Spectroscope	A non-destructive tool for detecting leaf moisture content by measuring the interaction of terahertz radiation with water molecules in fresh leaf samples [43].	Highly sensitive to water; fingerprinting capability allows for establishing high-precision prediction models (R² up to 0.979) for water status [43].
PlantRing Wearable Sensor	A flexible, high-throughput wearable sensor that measures stem or fruit circumference dynamics to monitor plant growth, water relations, and stress responses [10].	Made from carbonized silk gegette; offers high stretchability, durability, and sensitivity; enables large-scale quantification of stomatal sensitivity to soil drought [10].
Nutrient Solution (e.g., Yamazaki Formula)	Provides essential nutrients in controlled pot experiments, ensuring that observed plant responses are primarily due to the applied water stress and not nutrient deficiency [43].	Allows for precise control of nutrient levels, which is critical when studying the interaction between water stress and nitrogen content [43] [60].

The implementation of advanced sensor systems for drought stress detection in tomatoes represents a critical innovation in precision agriculture. Traditional monitoring methods often fail to detect early-stage water stress, leading to significant yield losses. Recent advancements in hyperspectral imaging (HSI) and deep learning (DL) models have revolutionized plant phenotyping and stress detection capabilities. This article provides a comprehensive benchmark of detection accuracies across multiple technological approaches, offering detailed application notes and experimental protocols for researchers developing sensor-based drought detection systems. By comparing quantitative performance metrics across hyperspectral and deep learning methodologies, we aim to establish robust benchmarks for the research community and facilitate the adoption of high-precision sensor systems in tomato cultivation.

Performance Benchmarking of Detection Technologies

The table below summarizes detection accuracies achieved by various imaging and modeling approaches for tomato drought stress detection, providing a quantitative benchmark for researchers.

Table 1: Benchmark of Detection Accuracies for Tomato Drought Stress

Technology Approach	Specific Methodology	Reported Accuracy	Key Performance Advantages
Hyperspectral Imaging	MLVI-CNN Framework (MLVI & H_VSI indices with 1D CNN)	83.40% classification accuracy [26] [17]	Detects stress 10-15 days earlier than conventional indices (r=0.98 correlation with ground truth) [26] [17]
Deep Learning for Phenotyping	Improved YOLOv11n with AKConv	98% classification accuracy for water stress conditions [2]	4.1% increased recall, 2.7% increase in mAP50, 5.4% increase in mAP50-95 for phenotype recognition [2]
Hyperspectral Imaging	ResNet and Transformer on HSI data	R² up to 0.96 for physicochemical properties [29]	Superior accuracy and robustness for quality parameter prediction [29]
Deep Learning for Defect Detection	Statistically rigorous benchmarking	AP50 of 0.823 for surface defects [62]	Statistical significance validation through ANOVA and Tukey's test [62]

Experimental Protocols

Hyperspectral Imaging with Machine Learning-Optimized Vegetation Indices

This protocol details the procedure for early drought stress detection in tomatoes using hyperspectral imaging combined with machine learning-optimized vegetation indices, achieving 83.40% classification accuracy for six stress severity levels [26] [17].

Materials and Equipment

• Hyperspectral imaging sensor (400-1700 nm range)
• Integration sphere for radiometric calibration
• MATLAB or Python with scikit-learn and TensorFlow/PyTorch
• White reference standard
• Computer with minimum 16GB RAM and GPU support

Step-by-Step Procedure

• Data Acquisition

  • Capture hyperspectral images of tomato plants across visible (406-1010 nm) and near-infrared (957-1677 nm) spectral ranges [29]
  • Maintain consistent illumination conditions using artificial light sources
  • Acquire images at regular intervals (recommended: daily) throughout growth cycle
  • Include white and dark reference frames for radiometric calibration

• Spectral Preprocessing

  • Apply radiometric calibration to convert raw digital numbers to reflectance values
  • Implement preprocessing algorithms: Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV), First Derivative, and Second Derivative [29]
  • Normalize spectral signatures to minimum 0 and maximum 1 to focus on spectral shape rather than absolute values [63]

• Feature Selection and Index Development

  • Execute Recursive Feature Elimination (RFE) to identify optimal spectral bands
  • Develop two novel hyperspectral indices:
    • Machine Learning-Based Vegetation Index (MLVI): Leverages critical NIR and SWIR bands
    • Hyperspectral Vegetation Stress Index (H_VSI): Optimized for early stress detection [26] [17]
  • Validate band selection through correlation analysis with ground-truth stress markers

• Model Training and Validation

  • Implement 1D CNN architecture with optimized hyperparameters through Bayesian optimization [29]
  • Configure network with convolutional layers, pooling layers, and fully connected layers
  • Train model using k-fold cross-validation (recommended: k=5) for robust performance estimation [63]
  • Validate against six levels of crop stress severity with statistical significance testing

Figure 1: Workflow for hyperspectral stress detection in tomatoes

Deep Learning-Based Phenotypic Trait Extraction

This protocol describes an improved YOLOv11n-based approach for automated phenotypic trait extraction from tomato images under drought stress conditions, achieving 98% classification accuracy for water stress levels [2].

Materials and Equipment

• RGB camera (minimum 12MP resolution)
• Controlled imaging environment with consistent lighting
• GPU-accelerated computing system (minimum NVIDIA GTX 1080)
• Python with PyTorch and OpenCV
• Measurement tools for ground truth validation (calipers, height meters)

Step-by-Step Procedure

• Image Acquisition and Dataset Preparation

  • Capture RGB images of tomato plants under varying water stress conditions
  • Maintain consistent camera distance and angle (recommended: 45-60 cm from plants)
  • Establish multiple water stress levels: control, mild stress, severe stress
  • Annotate images with bounding boxes for key phenotypic traits: plant height, petiole count, leaf number

• Model Architecture Enhancement

  • Implement improved YOLOv11n architecture with Adaptive Kernel Convolution (AKConv)
  • Integrate AKConv into backbone's C3 module with kernel size 2 convolution (C3k2)
  • Design recalibration feature pyramid detection head based on P2 layer for enhanced small object detection [2]
  • Configure model for multi-scale feature extraction to handle varying plant sizes

• Model Training and Optimization

  • Initialize with transfer learning from pre-trained weights
  • Apply data augmentation: rotation, flipping, brightness adjustment, and scaling
  • Optimize hyperparameters using grid search or Bayesian optimization
  • Train with stratified k-fold cross-validation to ensure representative sampling

• Phenotypic Parameter Computation and Stress Classification

  • Extract bounding box information for phenotypic trait measurement
  • Compute key parameters: plant height, petiole count, leaf number through geometric analysis
  • Validate accuracy against manual measurements (target: <10% relative error)
  • Implement Random Forest classifier with phenotypic traits as input features for stress level classification

Figure 2: Deep learning phenotyping workflow for tomato drought stress

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Research Materials for Tomato Drought Stress Detection Studies

Research Tool	Specifications	Application Function
Hyperspectral Imaging Systems	Spectral range: 400-1700 nm; Spatial resolution: ≥1024×1024; Spectral bands: ≥200 [63] [26]	Captures detailed spectral signatures for early stress detection through biochemical changes [26] [17]
UAV-Mounted HSI Platforms	Flight altitude: 10-50m; Ground resolution: 1-5cm/pixel; GPS/IMU integration [26]	Enables high-throughput field phenotyping and large-area stress monitoring [26]
Spectrophotometric Calibration Standards	White reference panels (≥99% reflectance); Dark reference targets [63] [29]	Ensures radiometric accuracy and consistency across imaging sessions [63]
Plant Wearable Sensors (PlantRing)	Nano-flexible carbonized silk georgette; Detection limit: 0.03%-0.17% strain; Tensile strain up to 100% [10]	Monitors plant growth and water status through organ circumference dynamics [10]
Deep Learning Frameworks	Python with TensorFlow/PyTorch; YOLOv11n implementation; Bayesian optimization tools [29] [2]	Enables automated phenotypic trait extraction and high-accuracy stress classification [2]

This benchmarking analysis demonstrates that both hyperspectral imaging and deep learning approaches offer substantial advantages for drought stress detection in tomatoes, with each technology excelling in specific applications. Hyperspectral imaging provides the earliest detection capabilities (10-15 days before visible symptoms) through sensitive spectral analysis, while deep learning phenotyping achieves superior classification accuracy (98%) through automated trait extraction. The protocols and benchmarks provided here establish a foundation for implementing these sensor systems in tomato research, enabling researchers to select appropriate technologies based on their specific accuracy, timing, and resource requirements. Future developments should focus on integrating these complementary technologies into unified sensor systems for comprehensive drought stress monitoring.

Conclusion

The integration of advanced sensor systems marks a paradigm shift in drought stress detection for tomatoes, moving from subjective assessment to quantitative, real-time physiological phenotyping. The synergy of wearable nanosensors, hyperspectral imaging, and AI-driven IoT platforms enables unprecedented resolution in monitoring stomatal behavior, water status, and growth dynamics. These technologies are not merely data collection tools but are catalyzing novel biological discoveries, such as the role of circadian clock genes in stomatal regulation and genotype-specific hydraulic mechanisms. For the research community, the future lies in harnessing these high-throughput phenotyping capabilities to close the genotype-phenotype gap, accelerate the development of drought-resilient cultivars, and realize fully autonomous, plant-based feedback irrigation systems that ensure sustainable tomato production in water-limited environments.

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