Ml-enhanced mechanistic crop modeling to address noise-induced uncertainty for drought environmental monitoring in rice
Year: 2025
Type: article
Abstract: 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. 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 higher estimations for grain yield (R2=0.99) and biomass (R2=0.8) in the northwest Tambacounda region of Senegal in Africa, providing reliable estimates of 30\% grain conversion efficiency and 2.18kg/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 (513mm, 49\%).
Source: Discover Food
Institutions: Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Pontificia Universidad Javeriana, Institut Agro Montpellier, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Amélioration Génétique et Adaptation des Plantes méditerranéennes et tropicales
Language: English
Subfield: Plant Science
Domain: Life Sciences
SDGs: Zero hunger, Climate action
Open Access status: gold
Estimated APC paid: $1,590
Awards: G2383164747, G7499547210, G8052866656
OpenAlex ID: W4414609690