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 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.

Plant phenotyping relevance

可視・熱画像と生物物理形質を統合した機械学習による、萎凋病条件下の遺伝子型別早期収量予測を開発・評価しており、形質推定手法が中心である。

Code and data availability

The paper's chickpea biophysical measurements, thermal/RGB image indices, and ML yield-prediction data are not publicly deposited; the Data availability statement says they are available only from the corresponding author upon reasonable request. No public code, images, or trained models are mentioned.

No evidence-backed public reproduction asset is currently recorded.