Integrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorAuburn University-
Autor(es): dc.contributorUniversidade Federal de Lavras (UFLA)-
Autor(es): dc.creatorSouza, Jarlyson Brunno Costa.-
Autor(es): dc.creatorde Almeida, Samira Luns Hatum.-
Autor(es): dc.creatorFreire de Oliveira, Mailson.-
Autor(es): dc.creatorDos Santos, Adão Felipe.-
Autor(es): dc.creatorFilho, Armando Lopes de Brito.-
Autor(es): dc.creatorMeneses, Mariana Dias.-
Autor(es): dc.creatorSilva, Rouverson Pereira da.-
Data de aceite: dc.date.accessioned2025-08-21T15:14:31Z-
Data de disponibilização: dc.date.available2025-08-21T15:14:31Z-
Data de envio: dc.date.issued2023-03-02-
Data de envio: dc.date.issued2023-03-02-
Data de envio: dc.date.issued2022-07-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/agronomy12071512-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/242146-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/242146-
Descrição: dc.descriptionThe monitoring and determination of peanut maturity are fundamental to reducing losses during digging operation. However, the methods currently used are laborious and subjective. To solve this problem, we developed models to access peanut maturity using images from unmanned aerial vehicles (UAV) and satellites. We evaluated an area of approximately 8 hectares in which a regular grid of 30 points was determined with weekly evaluations starting at 90 days after sowing. Two Artificial Neural Networking (ANN) were used with Radial Basis Function (RBF) and Multilayer Perceptron (MLP) to predict the Peanut Maturity Index (PMI) with the spectral bands available from each sensor. Several vegetation indices were used as input to the ANN, with the data being split 80/20 for training and validation, respectively. The vegetation index, Normalized Difference Red Edge Index (NDRE), was the most precise coefficient of determination (R2 = 0.88) and accurate mean absolute error (MAE = 0.06) for estimating PMI, regardless of the type of ANN used. The satellite with Normalized Difference Vegetation Index (NDVI) could also determine PMI with better accuracy (MAE = 0.05) than the NDRE. The performance evaluation indicates that the RBF and MLP networks are similar in predicting peanut maturity. We concluded that satellite and UAV images can predict the maturity index with good accuracy and precision.-
Descrição: dc.descriptionDepartment of Engineering and Mathematical Sciences School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp), SP-
Descrição: dc.descriptionDepartment of Crop Soil and Environmental Sciences Auburn University-
Descrição: dc.descriptionDepartment of Agriculture School of Agricultural Sciences of Lavras Federal University of Lavras (UFLA), MG-
Descrição: dc.descriptionDepartment of Engineering and Mathematical Sciences School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp), SP-
Idioma: dc.languageen-
Relação: dc.relationAgronomy-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectMultilayer Perceptron-
Palavras-chave: dc.subjectPlanetScope-
Palavras-chave: dc.subjectRadial Basis Function-
Palavras-chave: dc.subjectunmanned aerial vehicle-
Título: dc.titleIntegrating Satellite and UAV Data to Predict Peanut Maturity upon Artificial Neural Networks-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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