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 <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44187_2025_611_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\times \)</EquationSource> </InlineEquation> 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 (<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44187_2025_611_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="82" /> </InlineMediaObject> <EquationSource Format="TEX">\(r^2=0.9996\)</EquationSource> </InlineEquation>) and biomass (<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44187_2025_611_Article_IEq3.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="74" /> </InlineMediaObject> <EquationSource Format="TEX">\(r^2=0.809\)</EquationSource> </InlineEquation>) in the northwest Tambacounda region of Senegal in Africa, providing reliable estimates of <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44187_2025_611_Article_IEq4.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="31" /> </InlineMediaObject> <EquationSource Format="TEX">\(30\%\)</EquationSource> </InlineEquation> grain conversion efficiency and <InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44187_2025_611_Article_IEq5.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="30" /> </InlineMediaObject> <EquationSource Format="TEX">\(2.18\)</EquationSource> </InlineEquation> 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%).

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

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2025年9月29日

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Edgar S. Correa,
Francisco C. Calderon,
Julian D. Colorado

摘要

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 \(\times \) 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 ( \(r^2=0.9996\) ) and biomass ( \(r^2=0.809\) ) 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%).