Estimation of Biophysical Parameters of Forage Cactus Under Different Agricultural Systems Through Vegetation Indices and Machine Learning Using RGB Images Acquired with Unmanned Aerial Vehicles

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorFederal Rural University of Pernambuco-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFederal University of Campina Grande—UFCG-
Autor(es): dc.contributorGoiano Federal Institute-
Autor(es): dc.creatorSilva, Gabriel Italo Novaes da-
Autor(es): dc.creatorJardim, Alexandre Maniçoba da Rosa Ferraz-
Autor(es): dc.creatorSantos, Wagner Martins dos-
Autor(es): dc.creatorBezerra, Alan Cézar-
Autor(es): dc.creatorAlba, Elisiane-
Autor(es): dc.creatorSilva, Marcos Vinícius da-
Autor(es): dc.creatorSilva, Jhon Lennon Bezerra da-
Autor(es): dc.creatorSouza, Luciana Sandra Bastos de-
Autor(es): dc.creatorMarinho, Gabriel Thales Barboza-
Autor(es): dc.creatorMontenegro, Abelardo Antônio de Assunção-
Autor(es): dc.creatorSilva, Thieres George Freire da-
Data de aceite: dc.date.accessioned2025-08-21T21:07:51Z-
Data de disponibilização: dc.date.available2025-08-21T21:07:51Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/agriculture14122166-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/306768-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/306768-
Descrição: dc.descriptionThe objective of this study was to correlate the biophysical parameters of forage cactus with visible vegetation indices obtained by unmanned aerial vehicles (UAVs) and predict them with machine learning in different agricultural systems. Four experimental units were conducted. Units I and II had different plant spacings (0.10, 0.20, 0.30, 0.40, and 0.50 m) with East–West and North–South planting directions, respectively. Unit III had row spacings (1.00, 1.25, 1.50, and 1.75 m), and IV had cutting frequencies (6, 9, 12 + 6, and 18 months) with the clones “Orelha de Elefante Mexicana”, “Miúda”, and “IPA Sertânia”. Plant height and width, cladode area index, fresh and dry matter yield (FM and DM), dry matter content, and fifteen vegetation indices of the visible range were analyzed. The RGBVI and ExGR indices stood out for presenting greater correlations with FM and DM. The prediction analysis using the Random Forest algorithm, highlighting DM, which presented a mean absolute error of 1.39, 0.99, and 1.72 Mg ha−1 in experimental units I and II, III, and IV, respectively. The results showed potential in the application of machine learning with RGB images for predictive analysis of the biophysical parameters of forage cactus.-
Descrição: dc.descriptionFundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco-
Descrição: dc.descriptionDepartment of Agricultural Engineering Federal Rural University of Pernambuco, Dom Manoel de Medeiros Avenue, s/n, Dois Irmãos-
Descrição: dc.descriptionDepartment of Biodiversity Institute of Biosciences São Paulo State University—UNESP, Avenue 24A, 1515, SP-
Descrição: dc.descriptionAcademic Unit of Serra Talhada Federal Rural University of Pernambuco, Gregório Ferraz Nogueira Avenue, s/nPE-
Descrição: dc.descriptionDepartment of Forest Engineering Federal University of Campina Grande—UFCG, PB-
Descrição: dc.descriptionCerrado Irrigation Graduate Program Goiano Federal Institute, Campus Ceres, GO-154, km 218–Zona RuralGO-
Descrição: dc.descriptionDepartment of Biodiversity Institute of Biosciences São Paulo State University—UNESP, Avenue 24A, 1515, SP-
Descrição: dc.descriptionFundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco: BCT-0221-5.03/21-
Idioma: dc.languageen-
Relação: dc.relationAgriculture (Switzerland)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectautomated procedures-
Palavras-chave: dc.subjectExGR-
Palavras-chave: dc.subjectforage cactus-
Palavras-chave: dc.subjectRandom Forest-
Palavras-chave: dc.subjectRGBVI-
Palavras-chave: dc.subjectvisible vegetation indices-
Título: dc.titleEstimation of Biophysical Parameters of Forage Cactus Under Different Agricultural Systems Through Vegetation Indices and Machine Learning Using RGB Images Acquired with Unmanned Aerial Vehicles-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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