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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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

id	RepoAGROSAVIA41165
record_format	dspace
institution	Corporación Colombiana de Investigación Agropecuaria
collection	Repositorio AGROSAVIA
language	Inglés
topic	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
spellingShingle	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 Chaali, Nesrine Ramírez Gomez, Carlos Manuel Jaramillo Barrios, Camilo Ignacio Garr´e, Sarah Barrero, Oscar Ouazaa, Sofiane Calderon Carvajal, John Edinson Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production
description	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 capture the intricate relationship between soil properties and crop physiological processes. This research assessed the effectiveness of applying multivariate geostatistical analysis and unsupervised machine learning (UML) to geophysical and multispectral data through ECa, NDWI and NDVI indices, for delineating and validating the SSMZ at different crop cycles in five rice field of Tolima department-Colombia. Lee´s correlation was conducted between ECa, NDWI and NDVI data. MULTISPATI PCA was used to assess the autocorrelation in the input variables before SSMZ delineation. The correlation results revealed two field categories based on the direct or indirect relationship between ECa and the multispectral data. On one hand, ECa values showed an inverse correlation (p < 0.01) with multispectral data (-0.25 to -0.45) for fields 1a , 1b and 4a . On the other hand, there was a direct correlation (p < 0.01) between ECa and multispectral data (0.31 to 0.51) for fields 2b and 5a . For all rice fields, MULTISPATI-PCA explained a range between 75.01 % to 95.64 % of the total variability. UML results highlighted two, three and four SSMZ, with two SSMZ being the optimal management zones number. The validation suggests that the θ data showed a significant statistical difference for two SSMZs (p < 0.05), regardless of the water content ranges. This study emphasized the importance of integrating geophysical and multispectral datasets in precision agriculture for evaluating field variability and crop conditions.
format	article
author	Chaali, Nesrine Ramírez Gomez, Carlos Manuel Jaramillo Barrios, Camilo Ignacio Garr´e, Sarah Barrero, Oscar Ouazaa, Sofiane Calderon Carvajal, John Edinson
author_facet	Chaali, Nesrine Ramírez Gomez, Carlos Manuel Jaramillo Barrios, Camilo Ignacio Garr´e, Sarah Barrero, Oscar Ouazaa, Sofiane Calderon Carvajal, John Edinson
author_sort	Chaali, Nesrine
title	Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production
title_short	Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production
title_full	Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production
title_fullStr	Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production
title_full_unstemmed	Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production
title_sort	enhancing irrigation management: unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production
publisher	Elsevier
publishDate	2025
url	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
work_keys_str_mv	AT chaalinesrine enhancingirrigationmanagementunsupervisedmachinelearningcoupledwithgeophysicalandmultispectraldataforinformeddecisionmakinginriceproduction AT ramirezgomezcarlosmanuel enhancingirrigationmanagementunsupervisedmachinelearningcoupledwithgeophysicalandmultispectraldataforinformeddecisionmakinginriceproduction AT jaramillobarrioscamiloignacio enhancingirrigationmanagementunsupervisedmachinelearningcoupledwithgeophysicalandmultispectraldataforinformeddecisionmakinginriceproduction AT garresarah enhancingirrigationmanagementunsupervisedmachinelearningcoupledwithgeophysicalandmultispectraldataforinformeddecisionmakinginriceproduction AT barrerooscar enhancingirrigationmanagementunsupervisedmachinelearningcoupledwithgeophysicalandmultispectraldataforinformeddecisionmakinginriceproduction AT ouazaasofiane enhancingirrigationmanagementunsupervisedmachinelearningcoupledwithgeophysicalandmultispectraldataforinformeddecisionmakinginriceproduction AT calderoncarvajaljohnedinson enhancingirrigationmanagementunsupervisedmachinelearningcoupledwithgeophysicalandmultispectraldataforinformeddecisionmakinginriceproduction
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spelling	RepoAGROSAVIA411652025-08-30T03:01:16Z 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 Chaali, Nesrine Ramírez Gomez, Carlos Manuel Jaramillo Barrios, Camilo Ignacio Garr´e, Sarah Barrero, Oscar Ouazaa, Sofiane Calderon Carvajal, John Edinson 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 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 capture the intricate relationship between soil properties and crop physiological processes. This research assessed the effectiveness of applying multivariate geostatistical analysis and unsupervised machine learning (UML) to geophysical and multispectral data through ECa, NDWI and NDVI indices, for delineating and validating the SSMZ at different crop cycles in five rice field of Tolima department-Colombia. Lee´s correlation was conducted between ECa, NDWI and NDVI data. MULTISPATI PCA was used to assess the autocorrelation in the input variables before SSMZ delineation. The correlation results revealed two field categories based on the direct or indirect relationship between ECa and the multispectral data. On one hand, ECa values showed an inverse correlation (p < 0.01) with multispectral data (-0.25 to -0.45) for fields 1a , 1b and 4a . On the other hand, there was a direct correlation (p < 0.01) between ECa and multispectral data (0.31 to 0.51) for fields 2b and 5a . For all rice fields, MULTISPATI-PCA explained a range between 75.01 % to 95.64 % of the total variability. UML results highlighted two, three and four SSMZ, with two SSMZ being the optimal management zones number. The validation suggests that the θ data showed a significant statistical difference for two SSMZs (p < 0.05), regardless of the water content ranges. This study emphasized the importance of integrating geophysical and multispectral datasets in precision agriculture for evaluating field variability and crop conditions. Arroz-Oryza sativa 2025-08-29T20:54:46Z 2025-08-29T20:54:46Z 2024-12 2024 article Artículo científico http://purl.org/coar/resource_type/c_2df8fbb1 info:eu-repo/semantics/article https://purl.org/redcol/resource_type/ART http://purl.org/coar/version/c_970fb48d4fbd8a85 https://www-sciencedirect-com.recursos.agrosavia.co/science/article/pii/S2772375524002405?via%3Dihub 2772-3755 http://hdl.handle.net/20.500.12324/41165 https://doi.org/10.1016/j.atech.2024.100635 reponame:Biblioteca Digital Agropecuaria de Colombia instname:Corporación colombiana de investigación agropecuaria AGROSAVIA eng Smart Agricultural Technology 9 1 1 20 J. Munz, I. Maurmann, H. Schuele, R. Doluschitz, Digital transformation at what cost? A case study from Germany estimating the adoption potential of precision farming technologies under different scenarios, Smart Agric. Technol. (2024) 100585, https://doi.org/10.1016/j.atech.2024.100585. R. Abbasi, P. Martinez, R. 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Enhancing irrigation management: Unsupervised machine learning coupled with geophysical and multispectral data for informed decision-making in rice production

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