Assessment of Injury by Four Major Pests in Soybean Plants Using Hyperspectral Proximal Imaging

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFederal University of Viçosa-
Autor(es): dc.creatorIost Filho, Fernando Henrique-
Autor(es): dc.creatorPazini, Juliano de Bastos-
Autor(es): dc.creatorMedeiros, André Dantas de-
Autor(es): dc.creatorRosalen, David Luciano-
Autor(es): dc.creatorYamamoto, Pedro Takao-
Data de aceite: dc.date.accessioned2025-08-21T16:42:59Z-
Data de disponibilização: dc.date.available2025-08-21T16:42:59Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2022-07-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/agronomy12071516-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/240380-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/240380-
Descrição: dc.descriptionArthropod pests are among the major problems in soybean production and regular field sampling is required as a basis for decision-making for control. However, traditional sampling methods are laborious and time-consuming. Therefore, our goal is to evaluate hyperspectral remote sensing as a tool to establish reflectance patterns from soybean plants infested by various densities of two species of stinkbugs (Euschistus heros and Diceraeus melacanthus (Hemiptera: Pentatomidae)) and two species of caterpillars (Spodoptera eridania and Chrysodeixis includens (Lepidoptera: Noctuidae)). Bioassays were carried out in greenhouses with potted plants placed in cages with 5 plants infested with 0, 2, 5, and 10 insects. Plants were classified according to their reflectance, based on the acquisition of spectral data before and after infestation, using a hyperspectral push-broom spectral camera. Infestation by stinkbugs did not cause significative differences in the reflectance patterns of infested or non-infested plants. In contrast, caterpillars caused changes in the reflectance patterns, which were classified using a deep-learning approach based on a multilayer perceptron artificial neural network. High accuracies were achieved when the models classified low (0 + 2) or high (5 + 10) infestation and presence or absence of insects. This study provides an initial assessment to apply a non-invasive detection method to monitor caterpillars in soybean before causing economic damage.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionDepartment of Entomology and Acarology University of São Paulo-
Descrição: dc.descriptionDepartment of Rural Engineering São Paulo State University-
Descrição: dc.descriptionDepartment of Agronomy Federal University of Viçosa-
Descrição: dc.descriptionDepartment of Rural Engineering São Paulo State University-
Descrição: dc.descriptionFAPESP: 2017/19407-4-
Descrição: dc.descriptionFAPESP: 2019/26099-0-
Descrição: dc.descriptionFAPESP: 2019/26145-1-
Idioma: dc.languageen-
Relação: dc.relationAgronomy-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectcaterpillars-
Palavras-chave: dc.subjectGlycine max-
Palavras-chave: dc.subjectpest management-
Palavras-chave: dc.subjectsampling-
Palavras-chave: dc.subjectstinkbugs-
Título: dc.titleAssessment of Injury by Four Major Pests in Soybean Plants Using Hyperspectral Proximal Imaging-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.