Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production

Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production

Integrating diverse data sources for site-specific management zones (SSMZ) in precision agriculture is a complex task. Soil surveys using apparent electrical conductivity (ECa) have proven effective in capturing field variability. However, relying solely on one sensing data type may not fully ca...

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Detalles Bibliográficos
Autores principales: Chaali, Nesrine, Ramírez Gomez, Carlos Manuel, Jaramillo Barrios, Camilo Ignacio, Garr´e, Sarah, Barrero, Oscar, Ouazaa, Sofiane, Calderon Carvajal, John Edinson
Formato: article
Lenguaje:Inglés
Publicado: Elsevier 2025
Materias:
Investigación agropecuaria - A50
Oryza sativa
Producción
Población agraria
Gestión
Transitorios
http://aims.fao.org/aos/agrovoc/c_5438
http://aims.fao.org/aos/agrovoc/c_6200
http://aims.fao.org/aos/agrovoc/c_8676
http://aims.fao.org/aos/agrovoc/c_16086
Acceso en línea:https://www-sciencedirect-com.recursos.agrosavia.co/science/article/pii/S2772375524002405?via%3Dihub
http://hdl.handle.net/20.500.12324/41165
https://doi.org/10.1016/j.atech.2024.100635

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