Un nuevo marco de aprendizaje profundo para la predicción del rendimiento del trigo a escala de campo

A genetic algorithm-optimized deep neural network was developed using proximal sensing data to accurately predict wheat yield at field scale, outperforming traditional machine learning models under diverse conditions. Hand held or vehicle-mounted active proximal sensing technologies offer a rapid, non-destructive method for real-time crop monitoring through spectral vegetation indices. This study integrates such proximal sensing data into a deep learning framework for field-scale wheat yield prediction. Specifically, wheat yield is predicted using normalized difference vegetation index (NDVI), canopy temperature (CT), and plant height (PH) through a deep neural network (DNN) optimized using a genetic algorithm (GA). The model is trained on data from 3,350 diverse wheat germplasm grown under irrigated and rainfed conditions at two locations during the 2020-2021 winter season. Comparative analysis demonstrates that the GA-optimized DNN outperforms traditional machine learning models such as Random Forest Regression (RFR), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Regression (SVR). Among individual feature groups, NDVI measured at five wheat growth stages showing strong predictive capability, with R² values ≥ 60% under irrigated and ≥ 50% under rainfed conditions. Additionally, RFR is employed to identify the most influential features for predicting grain yield. This pioneering study introduces the first-ever application of a GA-optimized deep neural network, leveraging handheld or vehicle-mounted proximal sensing data for predicting crop yield, in the context of Indian agriculture. The proposed approach offers a robust and scalable solution for pre-harvest yield estimation, supporting breeders and researchers in efficient genotype selection and contributing to the achievement of sustainable development goals.