Classification of soybean groups for grain yield and industrial traits using Vnir-Swir spectroscopy

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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.contributorState University of Mato Grosso (UNEMAT)-
Autor(es): dc.contributorFaculty of Veterinary Medicine and Animal Science (FAMEZ)-
Autor(es): dc.creatorSantana, Dthenifer Cordeiro-
Autor(es): dc.creatorSeron, Ana Carina Candido-
Autor(es): dc.creatorTeodoro, Larissa Pereira Ribeiro-
Autor(es): dc.creatorde Oliveira, Izabela Cristina-
Autor(es): dc.creatorda Silva Junior, Carlos Antonio-
Autor(es): dc.creatorBaio, Fábio Henrique Rojo-
Autor(es): dc.creatorÍtavo, Camila Celeste Brandão Ferreira-
Autor(es): dc.creatorÍtavo, Luis Carlos Vinhas-
Autor(es): dc.creatorTeodoro, Paulo Eduardo-
Data de aceite: dc.date.accessioned2025-08-21T18:07:54Z-
Data de disponibilização: dc.date.available2025-08-21T18:07:54Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-06-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.infrared.2024.105326-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308705-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308705-
Descrição: dc.descriptionThis research aimed to evaluate the accuracy of machine learning techniques in distinguishing groups soybean genotypes according to grain industrial traits using hyperspectral reflectance of the leaves. A total of 32 soybean genotypes were evaluated and allocated in randomized blocks with four replications. At 60 days after emergence, spectral analysis was carried out on three leaf samples from each plot. The spectral analysis of the leaves was carried out with a hyperspectral sensor providing ranges from 350 to 2500 nm. Once the wavelengths were obtained, they were grouped into averages of representative intervals into bands. At the end of the crop cycle, grain yield was obtained, and subsequently the determination of carbohydrate, oil, and protein content. Initially, the genotypes were subjected to cluster analysis using the k-means algorithm and subsequently, the data was subjected to machine learning analysis, using six models: J48 Decision Trees (J48) and REPTree (DT), Random Forest (RF), Artificial Neural Networks (ANW), Logistic Regression (LR) and Support Vector Machine (SVM). Logistic regression (LR) was used as a reference point as it is a traditional regression algorithm. The clusters formed acted as the output of the models, while for the input of the models, two groups of data were used: the spectral variables (SV) obtained by the sensor (350–2500 nm) and the spectral averages of the bands selected (BS) (350–2200 nm). The use of machine learning techniques presented lower responses than the standard technique used in the work, that is, LR, which presented superiority in the classification of soybean genotypes in terms of industrial traits. The use of wavelengths provided better performance of the algorithms in the classification in relation to selected bands.-
Descrição: dc.descriptionDepartment of Agronomy State University of São Paulo (UNESP), SP-
Descrição: dc.descriptionFederal University of Mato Grosso do Sul (UFMS), MS-
Descrição: dc.descriptionDepartment of Geography State University of Mato Grosso (UNEMAT), MT-
Descrição: dc.descriptionFaculty of Veterinary Medicine and Animal Science (FAMEZ), MS-
Descrição: dc.descriptionDepartment of Agronomy State University of São Paulo (UNESP), SP-
Idioma: dc.languageen-
Relação: dc.relationInfrared Physics and Technology-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectLogistic regression-
Palavras-chave: dc.subjectNIR, SWIR-
Palavras-chave: dc.subjectWave-length-
Título: dc.titleClassification of soybean groups for grain yield and industrial traits using Vnir-Swir spectroscopy-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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