Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models
Abstract Crop health assessment and early yield predictions are highly crucial under biotic stress conditions for crop management and market planning by farmers and policy planners. The objective of this study was, therefore, to assess the impact of different levels of wilt disease on the biophysica...
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Nature Portfolio
2025-02-01
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| author | RN Singh P. Krishnan C. Bharadwaj Sonam Sah B. Das |
| author_facet | RN Singh P. Krishnan C. Bharadwaj Sonam Sah B. Das |
| author_sort | RN Singh |
| collection | DOAJ |
| description | Abstract Crop health assessment and early yield predictions are highly crucial under biotic stress conditions for crop management and market planning by farmers and policy planners. The objective of this study was, therefore, to assess the impact of different levels of wilt disease on the biophysical parameters of chickpea and developing machine learning (ML) models for early yield prediction. Field experiments were carried out over three years at the Indian Agricultural Research Institute research farm in New Delhi. Thermal and visible images were collected alongside the measurement of crop biophysical parameters, including leaf area index (LAI), photosynthesis, transpiration rate, stomatal conductance, relative leaf water content (RWC), membrane stability index (MSI), and NDVI, for 85 chickpea genotypes with varying levels of wilt resistance. ML models were developed for early yield prediction by combining visible and thermal image indices with biophysical parameters. The results showed that the canopy temperatures were directly correlated with increasing levels of wilt severity. Crop photosynthesis, stomatal conductance, transpiration, LAI, RWC, MSI, and NDVI dropped significantly with increasing levels of wilt severity. Yield reductions of 44-69% were observed in susceptible genotypes. Machine learning models were able to give accurate early yield predictions. The accuracy of the models increases as we move closer to the harvest. Ranking of the model’s performances indicated that XGB is the best model to predict chickpea yield under wilt conditions. NDVI was identified as most important variable for yield prediction. The findings of the study quantified the impacts of wilt on important crop biophysical parameters and highlighted the suitability of ML models in early yield prediction under different levels of disease severity. |
| format | Article |
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| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
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| spelling | doaj-art-9f994cccbbe643588690dff8aebf2a022025-02-09T12:35:07ZengNature PortfolioScientific Reports2045-23222025-02-0115111810.1038/s41598-025-87134-0Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning modelsRN Singh0P. Krishnan1C. Bharadwaj2Sonam Sah3B. Das4Division of Agricultural Physics, ICAR-Indian Agricultural Research InstituteDivision of Agricultural Physics, ICAR-Indian Agricultural Research InstituteDivision of Genetics, ICAR-Indian Agricultural Research InstituteICAR-National Institute of Abiotic Stress ManagementICAR-Central Coastal Agricultural Research InstituteAbstract Crop health assessment and early yield predictions are highly crucial under biotic stress conditions for crop management and market planning by farmers and policy planners. The objective of this study was, therefore, to assess the impact of different levels of wilt disease on the biophysical parameters of chickpea and developing machine learning (ML) models for early yield prediction. Field experiments were carried out over three years at the Indian Agricultural Research Institute research farm in New Delhi. Thermal and visible images were collected alongside the measurement of crop biophysical parameters, including leaf area index (LAI), photosynthesis, transpiration rate, stomatal conductance, relative leaf water content (RWC), membrane stability index (MSI), and NDVI, for 85 chickpea genotypes with varying levels of wilt resistance. ML models were developed for early yield prediction by combining visible and thermal image indices with biophysical parameters. The results showed that the canopy temperatures were directly correlated with increasing levels of wilt severity. Crop photosynthesis, stomatal conductance, transpiration, LAI, RWC, MSI, and NDVI dropped significantly with increasing levels of wilt severity. Yield reductions of 44-69% were observed in susceptible genotypes. Machine learning models were able to give accurate early yield predictions. The accuracy of the models increases as we move closer to the harvest. Ranking of the model’s performances indicated that XGB is the best model to predict chickpea yield under wilt conditions. NDVI was identified as most important variable for yield prediction. The findings of the study quantified the impacts of wilt on important crop biophysical parameters and highlighted the suitability of ML models in early yield prediction under different levels of disease severity.https://doi.org/10.1038/s41598-025-87134-0Image indicesCanopy temperatureLAIPhotosynthesisMembrane stabilitysRPI |
| spellingShingle | RN Singh P. Krishnan C. Bharadwaj Sonam Sah B. Das Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models Scientific Reports Image indices Canopy temperature LAI Photosynthesis Membrane stability sRPI |
| title | Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models |
| title_full | Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models |
| title_fullStr | Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models |
| title_full_unstemmed | Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models |
| title_short | Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models |
| title_sort | optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models |
| topic | Image indices Canopy temperature LAI Photosynthesis Membrane stability sRPI |
| url | https://doi.org/10.1038/s41598-025-87134-0 |
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