Ml-enhanced mechanistic crop modeling to address noise-induced uncertainty for drought environmental monitoring in rice

Advancing our understanding of environmental interactions in rice crops contributes to food production in water-limited regions. This paper proposes an integrated crop modeling architecture, demonstrating how machine-learning (ML) models enhance classic Mechanistic Crop Modeling (MCM) estimations by learning directly from environmental data. Here, we quantify the impact of noise-induced uncertainty on the CERES-Rice crop growth model, particularly relevant for drought-tolerant varieties that exhibit complex adaptation mechanisms, such as Nerica 4 (Oryza sativa × Oryza glaberrima hybrid). Environment characterization is achieved through a novel 3D Gaussian Mixture Model (GMM), offering enhanced precision and scalability when coupled with remote-sensing satellite-derived environmental data. By coupling both MCM and ML models, we achieved superior estimations for grain yield ( r²=0.9996 ) and biomass ( r²=0.809 ) in the northwest Tambacounda region of Senegal in Africa, providing reliable estimates of $$30%$$ grain conversion efficiency and $$2.18$$ kg/ha·mm water use efficiency from an environment characterized by sandy soils with high saturated hydraulic conductivity (1.1 cm/h) and the lowest regional precipitation (513 mm, 49%).