Classification of soybean genotypes for industrial traits using UAV multispectral imagery and machine learning

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Federal de Mato Grosso do Sul (UFMS)-
Autor(es): dc.contributorFederal University of Santa Maria-
Autor(es): dc.contributorUniversity of Passo Fundo-
Autor(es): dc.contributorState University of Mato Grosso (UNEMAT)-
Autor(es): dc.contributorLouisiana State University-
Autor(es): dc.creatorSantana, Dthenifer Cordeiro-
Autor(es): dc.creatorTeodoro, Larissa Pereira Ribeiro-
Autor(es): dc.creatorBaio, Fábio Henrique Rojo-
Autor(es): dc.creatorSantos, Regimar Garcia dos-
Autor(es): dc.creatorCoradi, Paulo Carteri-
Autor(es): dc.creatorBiduski, Bárbara-
Autor(es): dc.creatorSilva Junior, Carlos Antonio da-
Autor(es): dc.creatorTeodoro, Paulo Eduardo-
Autor(es): dc.creatorShiratsuchi, Luaciano Shozo-
Data de aceite: dc.date.accessioned2025-08-21T18:57:03Z-
Data de disponibilização: dc.date.available2025-08-21T18:57:03Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.rsase.2023.100919-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/246601-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/246601-
Descrição: dc.descriptionSoybean genotypes have distinct physicochemical characteristics, mainly regarding the oil and protein contents in the grains. The use of high-throughput phe-notyping technologies allied to data processing by machine learning algorithms facili-tates and can make it faster and more precise to obtain information about the charac-teristics of the grains. Thus, the objective of the study was to identify machine learning algorithms and inputs with better performance for classifying genotypes clustered based on industrial traits. The experiment was implemented in a randomized block design with two replicates. 103 F2 soybean populations were evaluated. Red, green, near-infrared, and infrared spectral bands and the vegetation indices NDVI, NDRE, GNDVI, SAVI, MSAVI, MCARI, EVI, and SCCCI were measured using UAV multispectral imagery. The industrial traits evaluated were: crude protein content, oil yield, and ash and fiber contents. Data were subjected to Pearson correlation analysis expressed by a correlation network. A genotype clustering based on industrial traits was performed using PCA and k-means algorithm, and then the clusters formed were used as output variables of the ML models, while three input configurations were tested: only spectral bands (B), only vegetation indices (VIs), and B + VIs. ML algorithms tested were: artificial neural net-work (ANN), decision tree algorithms J48 (J48), REPTree (DT), and RandomTree (Rt), random forest (RF), Support Vector Machine (SVM), and logistic regression (LR, used as control). Statistical metrics used to evaluate the accuracy of the models were per-centage of correct classification (CC) and F-score. ML algorithms that achieved the highest classification accuracies were ANN, DT and SVM. As for the inputs tested, the best results were obtained using only spectral bands.-
Descrição: dc.descriptionUniversidade Federal de Mato Grosso do Sul-
Descrição: dc.descriptionFundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionDepartment of Agronomy State University of São Paulo (UNESP), Ilha Solteira, SP-
Descrição: dc.descriptionFederal University of Mato Grosso do Sul (UFMS), Chapadão do Sul, MS-
Descrição: dc.descriptionDepartment of Agricultural Engineering Federal University of Santa Maria, Cachoeira do Sul, RS-
Descrição: dc.descriptionDepartment of Food Science and Technology University of Passo Fundo, RS-
Descrição: dc.descriptionDepartment of Geography State University of Mato Grosso (UNEMAT), MT-
Descrição: dc.descriptionLSU Agcenter School of Plant Environmental and Soil Sciences Louisiana State University, 307 Sturgis Hall-
Descrição: dc.descriptionDepartment of Agronomy State University of São Paulo (UNESP), Ilha Solteira, SP-
Descrição: dc.descriptionFundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul: 07/2022-
Descrição: dc.descriptionCNPq: 303767/2020-0-
Descrição: dc.descriptionCNPq: 306022/2021-4-
Descrição: dc.descriptionCNPq: 309250/2021-8-
Descrição: dc.descriptionFundação de Apoio ao Desenvolvimento do Ensino, Ciência e Tecnologia do Estado de Mato Grosso do Sul: 88/2021-
Idioma: dc.languageen-
Relação: dc.relationRemote Sensing Applications: Society and Environment-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectComputational intelligence-
Palavras-chave: dc.subjectHigh-throughput phenotyping-
Palavras-chave: dc.subjectPrecision agri-culture-
Palavras-chave: dc.subjectSpectral bands-
Palavras-chave: dc.subjectVegetation indices-
Título: dc.titleClassification of soybean genotypes for industrial traits using UAV multispectral imagery and machine learning-
Tipo de arquivo: dc.typelivro digital-
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