A Novel Deep Learning Framework for Field Scale Wheat Yield Prediction

Abstract

Abstract 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 indices (NDVIs), canopy temperatures (CTs), 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 2020–21 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, NDVIs 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 within each group. 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.

Code and data availability

The preprint describes a GA-optimized DNN for wheat yield prediction using proximal sensing data (NDVI, CT, PH) from ~3,350 wheat accessions, but the supplied blocks contain no data availability statement, no public dataset deposit, no code/model release, and no author-provided URL for any paper-specific asset. All DOI

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