Abstract
As global populations increase and economies expand, the demand for freshwater is surging, exacerbated by the effects of climate change and shifting lifestyles. It is resulting in widespread water stress and straining food production systems, a challenge anticipated to intensify in the coming decades. One potential solution to mitigate the impact of water scarcity, particularly in water-deficient regions, is the cultivation of water deficit stress-tolerant crop varieties. This study explores the simultaneous assessment of photosynthetic machinery and plant growth responses using chlorophyll fluorescence (ChlF) image based high-throughput phenotyping (HTP) for water deficit stress tolerance on 184 RILs in a controlled environment phenotyping facility. Under stress, recombinant inbred lines (RILs) displayed a diminished variable to maximum fluorescence ratio (Fv/Fm) compared to the control. However, stress-tolerant lines maintained higher Fv/Fm ratio and projected Fv/Fm area, mitigating water stress-induced yield losses. Machine learning using K-Nearest Neighbor, Support Vector Classifier and Random Forest, classified wheat RILs intro stress tolerance classes using sensor derived parameters with high accuracy of 0.56, 0.58 and 0.60 respectively. This study demonstrates the full potential of ChlF image-based phenotyping for enhanced throughput, identifying stress tolerance RILs as well as sensor derived traits, and novel indices. It emphasizes the importance of utilizing innovative data analytics techniques like PCA, clustering and machine learning to alleviate the data analysis bottleneck of HTP, for accelerating the pace of crop improvement for stress tolerance and sustainable food production.
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Data availability
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author. The code for implementation of machine learning is given in Github https://github.com/sunnyhyperspectral/HTP-ML-based-classification-of-RIL-for-stress-tolerance.
Abbreviations
- RIL:
-
Recombinant inbred lines
- ChlF:
-
Chlorophyll fluorescence
- ML:
-
Machine learning
- PCA:
-
Principal component analysis
- Fv/Fm:
-
Variable fluorescence by maximum fluorescence
- PFA:
-
Projected Fv/Fm area
- SVC:
-
Support vector classifier
- KNN:
-
K- nearest neighbour
- RF:
-
Random forest
- WCSS:
-
Within-cluster sum of square
- WD:
-
Water deficit
References
Aldea, M., Hamilton, J. G., Resti, J. P., Zangerl, A. R., Berenbaum, M. R., Frank, T. D., & DeLucia, E. H. (2006). Comparison of photosynthetic damage from arthropod herbivory and pathogen infection in understory hardwood saplings. Oecologia, 149, 221–232. https://doi.org/10.1007/s00442-006-0444-x
Bånkestad, D., & Wik, T. (2016). Growth tracking of basil by proximal remote sensing of chlorophyll fluorescence in growth chamber and greenhouse environments. Computers and Electronics in Agriculture, 128, 77–86. https://doi.org/10.1016/j.compag.2016.08.004
Barron, J., Enfors, E., Cambridge, H., & Moustapha, A. M. (2010). Coping with rainfall variability: Dry spell mitigation and implication on landscape water balances in small-scale farming systems in semi-arid Niger. International Journal of Water Resources Development, 26(4), 543–559. https://doi.org/10.1080/07900627.2010.519519
Blum, A. (1983). Genetic and physiological relationships in plant breeding for drought resistance. Agricultural Water Management, 7(1–3), 195–205. https://doi.org/10.1016/0378-3774(83)90083-5
Chaerle, L., Leinonen, I., Jones, H. G., & Van Der Straeten, D. (2007). Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. Journal of Experimental Botany, 58(4), 773–784. https://doi.org/10.1093/jxb/erl257
Clarke, J. M., DePauw, R. M., & Townley-Smith, T. F. (1992). Evaluation of methods for quantification of drought tolerance in wheat. Crop Science, 32(3), 723–728. https://doi.org/10.2135/cropsci1992.0011183x003200030029x
Drucker, H., Burges, C. J., Kaufman, L., Smola, A., & Vapnik, V. (1996). Support vector regression machines. Advances in Neural Information Processing Systems. https://doi.org/10.1007/3-540-61510-5_12
Evans, J. R. (1983). Nitrogen and photosynthesis in the flag leaf of wheat (Triticum aestivum L.). Plant Physiology, 72(2), 297–302. https://doi.org/10.1104/pp.72.2.297
Fahlgren, N., Gehan, M. A., & Baxter, I. (2015). Lights, camera, action: High-throughput plant phenotyping is ready for a close-up. Current Opinion in Plant Biology, 24, 93–99. https://doi.org/10.1016/j.pbi.2015.02.006
Falkenmark, M., & Rockström, J. (2004). Balancing water for humans and nature: The new approach in ecohydrology. Earthscan. https://doi.org/10.5860/choice.42-4012
FAO – Food and Agriculture Organization of the United Nations: FAOSTAT (2022)http://www.fao.org/faostat/en/#data/EL (last access : 9 March 2022)
Farshadfar, E., & Sutka, J. (2003). Multivariate analysis of drought tolerance in wheat substitution lines. Cereal Research Communications, 31, 33–40. https://doi.org/10.1007/bf03543247
Fernandez, G. C. (1992). Effective selection criteria for assessing plant stress tolerance. In Proceeding of the International Symposium on Adaptation of Vegetables and other Food Crops in Temperature and Water Stress, Aug. 13–16, Shanhua, Taiwan, 1992 (pp. 257–270). https://doi.org/10.15192/pscp.aab.2014.1.3.112123
Fischer, R. A., & Maurer, R. (1978). Drought resistance in spring wheat cultivars. I. Grain yield responses. Australian Journal of Agricultural Research, 29(5), 897–912. https://doi.org/10.1071/ar9780897
Fitton, N., Alexander, P., Arnell, N., Bajzelj, B., Calvin, K., Doelman, J., & Smith, P. (2019). The vulnerabilities of agricultural land and food production to future water scarcity. Global Environmental Change, 58, 101944. https://doi.org/10.1016/j.gloenvcha.2019.101944
Golabadi, M., Arzani, A. S. A. M., & Maibody, S. M. (2006). Assessment of drought tolerance in segregating populations in durum wheat. African Journal of Agricultural Research, 1(5), 162–171.
Grieder, C., Hund, A., & Walter, A. (2015). Image based phenotyping during winter: A powerful tool to assess wheat genetic variation in growth response to temperature. Functional Plant Biology, 42(4), 387–396. https://doi.org/10.1071/fp14226
Grigorova, B., Vassileva, V., Klimchuk, D., Vaseva, I., Demirevska, K., & Feller, U. (2012). Drought, high temperature, and their combination affect ultrastructure of chloroplasts and mitochondria in wheat (Triticum aestivum L.) leaves. Journal of Plant Interactions, 7(3), 204–213. https://doi.org/10.1080/17429145.2011.654134
Grzesiak, S., Hordyńska, N., Szczyrek, P., Grzesiak, M. T., Noga, A., & Szechyńska-Hebda, M. (2019). Variation among wheat (Triticum easativum L.) genotypes in response to the drought stress: I–selection approaches. Journal of Plant Interactions, 14(1), 30–44. https://doi.org/10.1080/17429145.2018.1550817
Hadebe, S. T., Modi, A. T., & Mabhaudhi, T. (2017). Drought tolerance and water use of cereal crops: A focus on sorghum as a food security crop in sub-Saharan Africa. Journal of Agronomy and Crop Science, 203(3), 177–191. https://doi.org/10.1111/jac.12191
Harbinson, J., Prinzenberg, A. E., Kruijer, W., & Aarts, M. G. (2012). High throughput screening with chlorophyll fluorescence imaging and its use in crop improvement. Current Opinion in Biotechnology, 23(2), 221–226. https://doi.org/10.1016/j.copbio.2011.10.006
Humplík, J. F., Lazár, D., Husičková, A., & Spíchal, L. (2015). Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses–a review. Plant Methods, 11(1), 1–10. https://doi.org/10.1186/s13007-015-0072-8
Jain, D., Ashraf, N., Khurana, J. P., & Shiva Kameshwari, M. N. (2019). The ‘omics’ approach for crop improvement against drought stress. Genetic Enhancement of Crops for Tolerance to Abiotic Stress: Mechanisms and Approaches, I, 183–204. https://doi.org/10.1007/978-3-319-91956-0_8
Jansen, M., Gilmer, F., Biskup, B., Nagel, K. A., Rascher, U., Fischbach, A., & Walter, A. (2009). Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants. Functional Plant Biology, 36(11), 902–914. https://doi.org/10.1071/fp09095
Kaya, Y., Palta, C., & Taner, S. (2002). Additive main effects and multiplicative interactions analysis of yield performances in bread wheat genotypes across environments. Turkish Journal of Agriculture and Forestry, 26(5), 275–279.
Lan, J. (1998). Comparison of evaluating methods for agronomic drought resistance in crops. Acta Agriculturae Boreali-Occidentalis Sinica, 7, 85–87.
McDaniel, R. L., Munster, C., & Nielsen-Gammon, J. (2017). Crop and location specific agricultural drought quantification: part III. Forecasting water stress and yield trends. Transactions of the ASABE, 60(3), 741–752. https://doi.org/10.13031/trans.11651
Naroui, R. M. R., Keykha, G., Abbaskoohpayegani, J., & Rafezi, R. (2020). Machine learning approaches to classify melon landraces based on phenotypic traits. Genetika, 52(3), 1021–1029. https://doi.org/10.2298/gensr2003021n
Pedregosa, F. (2011). Scikit-learn: Machine learning in python Fabian. Journal of Machine Learning Research, 12, 2825.
Porcar-Castell, A., Tyystjärvi, E., Atherton, J., Van der Tol, C., Flexas, J., Pfündel, E. E., & Berry, J. A. (2014). Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. Journal of Experimental Botany, 65(15), 4065–4095. https://doi.org/10.1093/jxb/eru191
Pradhan, G. P., Prasad, P. V. V., Fritz, A. K., Kirkham, M. B., & Gill, B. S. (2012). Effects of drought and high temperature stress on synthetic hexaploid wheat. Functional Plant Biology: FPB, 39(3), 190–198. https://doi.org/10.1071/FP11245
Priya, P., Patil, M., Pandey, P., Singh, A., Babu, V. S., & Senthil-Kumar, M. (2022). Stress combinations and their interactions in plants database (SCIPDb): a one-stop resource for understanding combined stress responses in plants. BioRxiv, 2022–12.
Ramirez-Vallejo, P., & Kelly, J. D. (1998). Traits related to drought resistance in common bean. Euphytica, 99, 127–136. https://doi.org/10.1023/a:1018353200015
Ripl, W. (2003). Water: the bloodstream of the biosphere. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 358(1440), 1921–1934. https://doi.org/10.1098/rstb.2003.1378
Rosa, L., Chiarelli, D. D., Rulli, M. C., Dell’Angelo, J., & D’Odorico, P. (2020). Global agricultural economic water scarcity. Science Advances, 6(18), eaaz6031. https://doi.org/10.1126/sciadv.aaz6031
Rosielle, A. A., & Hamblin, J. (1981). Theoretical aspects of selection for yield in stress and non-stress environment 1. Crop Science, 21(6), 943–946. https://doi.org/10.2135/cropsci1981.0011183x002100060033x
Scheuber, M. (2010). Potentials and limits of the k-nearest-neighbour method for regionalising sample-based data in forestry. European Journal of Forest Research, 129(5), 825–832. https://doi.org/10.1007/s10342-009-0290-6
Schurr, U., Walter, A., & Rascher, U. (2006). Functional dynamics of plant growth and photosynthesis–from steady-state to dynamics–from homogeneity to heterogeneity. Plant, Cell & Environment, 29(3), 340–352. https://doi.org/10.1111/j.1365-3040.2005.01490.x
Shangguan, Z., Shao, M., & Dyckmans, J. (2000). Effects of nitrogen nutrition and water deficit on net photosynthetic rate and chlorophyll fluorescence in winter wheat. Journal of Plant Physiology, 156(1), 46–51. https://doi.org/10.1016/s0176-1617(00)80271-0
Sun, D., Zhu, Y., Xu, H., He, Y., & Cen, H. (2019). Time-series chlorophyll fluorescence imaging reveals dynamic photosynthetic fingerprints of sos mutants to drought stress. Sensors, 19(12), 2649. https://doi.org/10.3390/s19122649
Thomas, H., Dalton, S. J., Evans, C., Chorlton, K. H., & Thomas, I. D. (1995). Evaluating drought resistance in germplasm of meadow fescue. Euphytica, 92, 401–411. https://doi.org/10.1007/bf00037125
Tsai, Y. C., Chen, K. C., Cheng, T. S., Lee, C., Lin, S. H., & Tung, C. W. (2019). Chlorophyll fluorescence analysis in diverse rice varieties reveals the positive correlation between the seedlings salt tolerance and photosynthetic efficiency. BMC Plant Biology, 19, 1–17. https://doi.org/10.1186/s12870-019-1983-8
Üstün, B., Melssen, W. J., & Buydens, L. M. C. (2006). Facilitating the application of support vector regression by using a universal Pearson VII function based kernel. Chemometrics and Intelligent Laboratory Systems, 81, 29–40. https://doi.org/10.1016/j.chemolab.2005.09.003
Acknowledgements
All authors acknowledge ICAR-Indian Agricultural Research Institute, New Delhi, for providing research facilities to conduct the research. SA is thankful to the Indian Agriculture Research Institute, University Grant Commission for Junior research fellowship and National Agricultural Higher Education Project (NAHEP)- Centres for Advanced Agricultural Sciences and Technology (NAHEP-CAAST) fund during his Ph.D. studies.
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The manuscript was reviewed and approved for publication by all authors. RNS, VC, SaK, SuK, KKB, KMM, RGR and SA: Conceptualization and designing of experiment. SA: Final data analysis, interpretation and writing-original draft. SA, RNS, and SuK: Experiment setup, data collection and data input. SA, RNS and SuK: Formal data analysis and visualization. VKS, KB, VC and KMM: Performance evaluation and review of whole experiment. SG, RNS, RGR and SK: Revision of manuscript. All authors contributed to the article and approved the submitted version.
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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this publication. The author declares the following financial interest/personal relationship which maybe considered as potential competing interests: Sunny Arya reports financial support was provided by Indian Agricultural Research Institute, UGC fellowship and NAHEP-CAAST fund. Sunny Arya reports a relationship with Indian Council of Agricultural Research that includes: employment.
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Arya, S., Sahoo, R.N., Sehgal, V.K. et al. High-throughput chlorophyll fluorescence image-based phenotyping for water deficit stress tolerance in wheat. Plant Physiol. Rep. (2024). https://doi.org/10.1007/s40502-024-00783-7
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DOI: https://doi.org/10.1007/s40502-024-00783-7