Field-Scale Rice Yield Prediction in Northern Coastal Region of Peru Using Sentinel-2 Vegetation Indices and Machine Learning Models

Accurate rice yield prediction is essential for optimizing water management and supporting decision-making in agricultural systems, particularly in arid environments where irrigation efficiency is critical. This study assessed five machine learning algorithms—Multiple Linear Regression (MLR), Suppor...

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Autores principales:	Isabel Jarro-Espinal, José Huanuqueño-Murillo, Javier Quille-Mamani, David Quispe-Tito, Lia Ramos-Fernández, Edwin Pino-Vargas, Alfonso Torres-Rua
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	MDPI AG 2025-09-01
Colección:	Agriculture
Materias:	Sentinel-2 vegetation indices (VIs) rice yield prediction machine learning models multiple linear regression (MLR) support vector regression (SVR)
Acceso en línea:	https://www.mdpi.com/2077-0472/15/19/2054
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