POTENTIAL OF MULTISPECTRAL IMAGES TAKEN BY SENSORS EMBEDDED IN UAVS FOR MONITORING THE COFFEE CROP IRRIGATION

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.creatorOrlando, Vinicius Silva Werneck-
Autor(es): dc.creatorMartins, George Deroco-
Autor(es): dc.creatorFraga, Eusimio Felisbino-
Autor(es): dc.creatorMarra, Aline Barrocá-
Autor(es): dc.creatorPereira, Fernando Vasconcelos-
Autor(es): dc.creatorde Lourdes Bueno Trindade Galo, Maria-
Data de aceite: dc.date.accessioned2025-08-21T21:34:45Z-
Data de disponibilização: dc.date.available2025-08-21T21:34:45Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-12-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.5194/isprs-annals-X-1-W1-2023-91-2023-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307748-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307748-
Descrição: dc.descriptionLeaf Water Potential (LWP) is an indicator widely used to understand water relations in a coffee tree. Monitoring water potential is a challenge for remote sensing using low-cost multispectral cameras, with images taken by remotely piloted aircraft. The objective of this work was to evaluate the potential of a low-cost camera to discriminate different water treatments in the coffee tree. In addition, the accuracy of models to estimate LWP in the coffee crop was evaluated. The results showed that the NDVI (Normalized Difference Vegetation Index) vegetation index was able to discriminate 61.6 % more plots in a drought regime than the Near-InfraRed (NIR) band in the rainfall regime. For LWP, the architecture that presented the best performance in the detection of water stress was for the first flight (SMOreg algorithm using as predictor variables all bands, Red, Green, and NIR, and the NDVI vegetation index) with RMSE value of 0.1880 and RMSE% of 34.18. For the second flight (Random Tree algorithm, using as predictor variables all bands and NDVI) with RMSE (0.0520) and RMSE% (32.00) values.-
Descrição: dc.descriptionUniversidade Federal de Uberlândia-
Descrição: dc.descriptionSão Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionFederal University of Uberlândia (UFU), Minas Gerais-
Descrição: dc.descriptionSão Paulo State University (UNESP), São Paulo-
Formato: dc.format91-96-
Idioma: dc.languageen-
Relação: dc.relationISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAgriculture-
Palavras-chave: dc.subjectCoffee Crop-
Palavras-chave: dc.subjectIrrigation-
Palavras-chave: dc.subjectLeaf Water Potential-
Palavras-chave: dc.subjectLow-Cost Images-
Palavras-chave: dc.subjectMachine Learning-
Título: dc.titlePOTENTIAL OF MULTISPECTRAL IMAGES TAKEN BY SENSORS EMBEDDED IN UAVS FOR MONITORING THE COFFEE CROP IRRIGATION-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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