Machine learning for classification of soybean populations for industrial technological variables based on agronomic traits

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de Mato Grosso do Sul (UFMS)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
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.contributorUniversidade Estadual de Mato Grosso do Sul (UEMS)-
Autor(es): dc.creatorTeodoro, Larissa Pereira Ribeiro-
Autor(es): dc.creatorSilva, Maik Oliveira-
Autor(es): dc.creatordos Santos, Regimar Garcia-
Autor(es): dc.creatorde Alcântara, Júlia Ferreira-
Autor(es): dc.creatorCoradi, Paulo Carteri-
Autor(es): dc.creatorBiduski, Bárbara-
Autor(es): dc.creatorda Silva Junior, Carlos Antonio-
Autor(es): dc.creatorTorres, Francisco Eduardo-
Autor(es): dc.creatorTeodoro, Paulo Eduardo-
Data de aceite: dc.date.accessioned2025-08-21T16:43:09Z-
Data de disponibilização: dc.date.available2025-08-21T16:43:09Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-03-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s10681-024-03301-w-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/306336-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/306336-
Descrição: dc.descriptionA current challenge of genetic breeding programs is to increase grain yield and protein content and at least maintain oil content. However, evaluations of industrial traits are time and cost-consuming. Thus, achieving accurate models for classifying genotypes with better industrial technological performance based on easier and faster to measure traits, such as agronomic ones, is of paramount importance for soybean breeding programs. The objective was to classify groups of soybean genotypes to industrial technological variables based on agronomic traits measured in the field using machine learning (ML) techniques. Field experiments were carried out in two sites in a randomized block design with two replications and 206 F2 soybean populations. Agronomic traits evaluated were: days to maturation (DM), first pod height (FPH), plant height (PH), number of branches (NB), main stem diameter (SD), mass of one hundred grains (MHG), and grain yield (GY). Industrial technological variables evaluated were oil yield, crude protein, crude fiber, and ash contents, determined by high-optical accuracy near-infrared spectroscopy (NIRS). The models tested were: support vector machine (SVM), artificial neural network (ANN), decision tree models J48 and REPTree, random forest (RF), and logistic regression (LR, used as control). A genotype clustering was performed using PCA and k-means algorithm, and then the clusters formed were used as output variables of the ML models, while the agronomic traits were used as input variables. ML techniques provided accurate models to classify soybean genotypes for more complex variables (industrial technological) based on agronomic traits. RF outperformed the other models and can be used to contribute to soybean breeding programs by classifying genotypes for industrial technological traits.-
Descrição: dc.descriptionFederal University of Mato Grosso Do Sul (UFMS), MS-
Descrição: dc.descriptionDepartment of Agronomy State University of São Paulo (UNESP), SP-
Descrição: dc.descriptionDepartment of Agricultural Engineering Federal University of Santa Maria, 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.descriptionState University of Mato Grosso Do Sul (UEMS), MS-
Descrição: dc.descriptionDepartment of Agronomy State University of São Paulo (UNESP), SP-
Idioma: dc.languageen-
Relação: dc.relationEuphytica-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectFiber-
Palavras-chave: dc.subjectGlycine max (L.) Merril-
Palavras-chave: dc.subjectOil-
Palavras-chave: dc.subjectProtein-
Palavras-chave: dc.subjectRandom forest-
Título: dc.titleMachine learning for classification of soybean populations for industrial technological variables based on agronomic traits-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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