Phenotyping Common Bean Under Drought Stress: High-Throughput Approaches for Enhanced Drought Tolerance
Abstract
:1. Introduction
2. Key Developmental, Morphological, Physiological, and Molecular Responses to Drought Stress in Common Bean
2.1. Root Morphology
2.2. Stem Morphology
2.3. Leaf Morphology
2.4. Generative Organs
2.5. Stomatal Conductance and Transpiration
2.6. Photosynthetic Pigments
2.7. Light Reaction and Calvin Cycle
2.8. Osmotic Adjustment
2.9. Antioxidant Activity
2.10. Phytohormones
2.11. Molecular Response
2.12. Bean Phenology
3. The Role of High-Throughput Phenotyping Techniques in Common Bean Breeding for Drought Tolerance
3.1. RGB Imaging
3.2. Multispectral and Hyperspectral Imaging
Index | Abbreviation | Equation | Utilization | Reference |
---|---|---|---|---|
Anthocyanin Index | ARI | ARI = (RGreen)−1 − (RFarRed)−1 | Assessment of anthocyanin content | [174] |
Chlorophyll Index | CHI | CHI = (RChl)−1 − (RNIR)−1 | Assessment of chlorophyll content | [151] |
Drought Severity Index | DSI | Calculation is based on the model which uses relatively fine-scale (1 km resolution) NDVI inputs from Moderate Resolution Imaging Spectroradiometer (MODIS) | Agricultural drought monitoring and early warning | [175] |
Green Normalized Difference Vegetation Index | GNDVI | GNDVI = (RNIR − RGreen)/(RNIR + GReen) | Vegetation health assessment and yield prediction | [176] |
Modified Chlorophyll Absorption in Reflectance Index | MCARI1 | MCAR1 = 1.2 × (2.5 × (RNIR − RRed) − 1.3 × (RNIR − RGreen)) | Assessment of vegetation health, biomass, and chlorophyll content | [177] |
Moisture Stress Index | MSI | MSI = 1600 nm/820 nm | Determines leaf and canopy water content | [178] |
Normalized Difference Drought Index | NDDI | NDDI = NDVI − NDWI/NDVI + NDWI | More sensitive indicator of drought than the NDVI | [179] |
Normalized Difference Water Index | NDWI | NDWI = (860 nm − 1240 nm)/(860 nm + 1240 nm) | Monitoring changes in the water content of plants | [180] |
Optimized Soil-Adjusted Vegetation Index | OSAVI | OSAVI = RNIR − RRed/RNIR + RRed + 0.16 | Assessment of vegetation health | [181] |
Normalized Difference Vegetation Index | NDVI | NDVI = (RNIR − RRed)/(RNIR + RRed) | Assessment of vegetation, phenophase, and biomass | [163] |
Water Band Index | WBI | WBI = 950 nm/900 nm | Assessment of water stress and water use efficiency | [165] |
3.3. Chlorophyll Fluorescence Imaging
Abbreviation | Trait | Equation |
---|---|---|
Fv/Fm | Maximum Efficiency of Photosystem II | Fv/Fm = (Fm − F0)/Fm [186] |
Fq’/Fm’ | Effective Quantum Yield of Photosystem II | Fq’/Fm’ = (Fm’ − Fs’)/Fm’ [187] |
rETR | Relative Electron Transport Rate | ETR = Fq’/Fm’ × PPFD × (0.5) [187] |
NPQ | Non-Photochemical Quenching | NPQ = (Fm − Fm’)/Fm’ [188] |
qP | Coefficient of Photochemical Quenching | qP = (Fm’ − Fs)/Fv [189] |
qN | Coefficient of Non-Photochemical Quenching | qN = 1 − (Fm’ − Fo’)/(Fm − Fo) [189] |
3.4. Three-Dimensional (3D) Imaging
3.5. Thermal Imaging
3.6. Root Phenotyping
4. Conclusion and Perspectives: Towards an Effective Phenotyping Strategy for Drought Tolerance in Common Bean
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
ABA | Abscisic acid |
APX | Ascorbate peroxidase |
ARI | Anthocyanin Index |
ATP | Adenosine triphosphate |
CAT | Catalase |
CF | Chlorophyll fluorescence |
CHI | Chlorophyll Index |
CT | X-ray computed tomography |
CYPs | Cytochrome P450 |
DHAR | Dehydroascorbate reductase |
DSI | Drought Severity Index |
ETC | Electron transport chain |
F0 | Minimum chlorophyll fluorescence of dark-adapted plants |
F0′ | Minimum fluorescence yield of illuminated plant |
Fm | Maximum chlorophyll fluorescence of dark-adapted plants |
Fm’ | Maximum chlorophyll fluorescence of light-adapted plants |
Fq’/Fm’ | Effective Quantum Yield of Photosystem II |
FS’ | Steady-state fluorescence yield |
Fv/Fm | Maximum Efficiency of Photosystem II |
GNDVI | Green Normalized Difference Vegetation Index |
GPX | Glutathione peroxidase |
GR | Glutathione reductase |
GSTs | Glutathione S-transferase |
GWAS | Genome-wide association studies |
HNBs | Hyperspectral narrowbands |
HTP | Hight-throughput phenotyping |
HVIs | Hyperspectral vegetation indices |
IRGA | Infrared gas analyzer |
JA | Jasmonic acid |
LAI | Leaf area index |
LEADER | Leaf Element Accumulation from Deep Roots |
LHC | Light-harvesting complex |
LiDAR | Light Detection and Ranging |
LWIR | Long-wave infrared |
MAPK | Mitogen-activated protein kinase |
MCARI1 | Modified Chlorophyll Absorption in Reflectance Index |
MDHAR | Monodehydroascorbate reductase |
MRI | Magnetic resonance imaging |
MSI | Moisture Stress Index |
MVS | Multi-view stereo |
NADPH | Nicotinamide adenine dinucleotide phosphate |
NCED | 9-cis-epoxycarotenoid dioxygenase |
NDDI | Normalized Difference Drought Index |
NDVI | Normalized Difference Vegetation Index |
NDWI | Normalized Difference Water Index |
NO | Nitric oxide |
NPQ | Non-photochemical quenching |
ɸno | Quantum yield of non-regulated non-photochemical energy loss in PSII |
OEC | Oxygen-evolving complex |
OEE | Oxygen-evolving enhancer |
PAR | Photosynthetically active radiation |
POD | Peroxidase |
PYR | Pyrabactin resistance |
qP | Coefficient of photochemical quenching |
qN | Coefficient of non-photochemical quenching |
rETR | Relative electron transport rate |
RGB | Red–green–blue |
ROS | Reactive oxygen species |
RuBisCO | Ribulose-1,5-diphosphate carboxylase/oxygenase |
RuBP | Ribulose bisphosphate |
SfM | Structure-from-motion |
SNP | Single-nucleotide polymorphism |
SOD | Superoxide dismutase |
TRX | Thioredoxin |
UAV | Unmanned aerial vehicle |
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Environment | Media | Methods | Advantages | Disadvantages |
---|---|---|---|---|
Field Phenotyping | Soil | Shovelomics, soil coring/washing/breaking, trenching, minirhizotrons | - Plants grow under natural environmental conditions - Provides the most representative physiological response - Reflects realistic root development | - Labor-intensive - Requires multiple steps such as soil riddling and washing - Potential root damage and loss - Repeated measurements on the same plant are not possible - High variability due to soil and climate differences |
Laboratory/Greenhouse Phenotyping | Substrate based systems | Pots, tubes, boxes, rhizoboxes, root chambers | - Easier experimental control and monitoring - Allows for more measurements in less time - Requires less root preparation before measurement - Lower risk of root damage during handling and extraction | - Root growth is restricted by container size - Possible root breakage during extraction and washing - Growth conditions are not fully representative of field conditions |
Soilless, transparent systems | Agar plates, gel-based systems, “pouch-and-wick” systems, hydroponics, aeroponics | - Provides easy root access - Eliminates the need for extensive root cleaning - Allows for repeated measurements on the same plant - Highly reproducible | - Lacks natural environmental influences on root development - Requires further validation for physiological relevance |
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Javornik, T.; Carović-Stanko, K.; Gunjača, J.; Šatović, Z.; Vidak, M.; Safner, T.; Lazarević, B. Phenotyping Common Bean Under Drought Stress: High-Throughput Approaches for Enhanced Drought Tolerance. Agronomy 2025, 15, 1344. https://doi.org/10.3390/agronomy15061344
Javornik T, Carović-Stanko K, Gunjača J, Šatović Z, Vidak M, Safner T, Lazarević B. Phenotyping Common Bean Under Drought Stress: High-Throughput Approaches for Enhanced Drought Tolerance. Agronomy. 2025; 15(6):1344. https://doi.org/10.3390/agronomy15061344
Chicago/Turabian StyleJavornik, Tomislav, Klaudija Carović-Stanko, Jerko Gunjača, Zlatko Šatović, Monika Vidak, Toni Safner, and Boris Lazarević. 2025. "Phenotyping Common Bean Under Drought Stress: High-Throughput Approaches for Enhanced Drought Tolerance" Agronomy 15, no. 6: 1344. https://doi.org/10.3390/agronomy15061344
APA StyleJavornik, T., Carović-Stanko, K., Gunjača, J., Šatović, Z., Vidak, M., Safner, T., & Lazarević, B. (2025). Phenotyping Common Bean Under Drought Stress: High-Throughput Approaches for Enhanced Drought Tolerance. Agronomy, 15(6), 1344. https://doi.org/10.3390/agronomy15061344