A neural network approach employed to classify soybean plants using multi-sensor images

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Autor(es): dc.contributorUniversity of Connecticut-
Autor(es): dc.contributorLouisiana State University-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorPrecision AgX-
Autor(es): dc.contributorUniversity Center of Herminio Ometto Foundation-
Autor(es): dc.contributorUniversity of Georgia-
Autor(es): dc.creatorde Souza, Flávia Luize Pereira-
Autor(es): dc.creatorShiratsuchi, Luciano Shozo-
Autor(es): dc.creatorDias, Maurício Acconcia-
Autor(es): dc.creatorBarbosa Júnior, Marcelo Rodrigues-
Autor(es): dc.creatorSetiyono, Tri Deri-
Autor(es): dc.creatorCampos, Sérgio-
Autor(es): dc.creatorTao, Haiying-
Data de aceite: dc.date.accessioned2025-08-21T15:35:30Z-
Data de disponibilização: dc.date.available2025-08-21T15:35:30Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2025-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s11119-025-10229-1-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/304758-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/304758-
Descrição: dc.descriptionCounting soybean plants is a crucial strategy for assessing sowing quality and supporting high production. Despite its importance, the laborious nature of traditional assessment methods makes them unreliable and not scalable. Additionally, innovative image-based solutions have demonstrated limitations in detecting dense crops such as soybeans. Therefore, in this study, we developed neural network models to analyze a set of RGB and multispectral images and perform plant classification in a comprehensive dataset, which included data collected at three vegetative stages of soybean (VC, V1, and V2). Our results demonstrated high accuracy in classifying plants using either RGB (98%) or multispectral images (92%). A significant strength of this study is the ability to classify highly dense plants, without a trend for misclassification. Clearly, our findings provide stakeholders with a timely and effective approach to counting soybean plants, reducing labor and time, while increasing reliability.-
Descrição: dc.descriptionDepartment of Plant Science and Landscape Architecture University of Connecticut, Storrs-
Descrição: dc.descriptionSchool of Plant Environmental and Soil Sciences Louisiana State University-
Descrição: dc.descriptionDepartment of Rural Engineering São Paulo State University, SP-
Descrição: dc.descriptionPrecision AgX, PO box 9617-
Descrição: dc.descriptionUniversity Center of Herminio Ometto Foundation, SP-
Descrição: dc.descriptionDepartment of Horticulture University of Georgia-
Descrição: dc.descriptionDepartment of Rural Engineering São Paulo State University, SP-
Idioma: dc.languageen-
Relação: dc.relationPrecision Agriculture-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDense crop-
Palavras-chave: dc.subjectMultilayer perceptron-
Palavras-chave: dc.subjectMultispectral images-
Palavras-chave: dc.subjectPlant classification-
Palavras-chave: dc.subjectStand count-
Título: dc.titleA neural network approach employed to classify soybean plants using multi-sensor images-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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