Remote Prediction of Soybean Yield Using UAV-Based Hyperspectral Imaging and Machine Learning Models

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Western São Paulo (UNOESTE)-
Autor(es): dc.contributorFinnish Geospatial Research Institute (FGI)-
Autor(es): dc.creatorBerveglieri, Adilson-
Autor(es): dc.creatorImai, Nilton Nobuhiro-
Autor(es): dc.creatorWatanabe, Fernanda Sayuri Yoshino-
Autor(es): dc.creatorTommaselli, Antonio Maria Garcia-
Autor(es): dc.creatorEderli, Glória Maria Padovani-
Autor(es): dc.creatorde Araújo, Fábio Fernandes-
Autor(es): dc.creatorLupatini, Gelci Carlos-
Autor(es): dc.creatorHonkavaara, Eija-
Data de aceite: dc.date.accessioned2025-08-21T18:49:12Z-
Data de disponibilização: dc.date.available2025-08-21T18:49:12Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-09-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/agriengineering6030185-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/298264-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/298264-
Descrição: dc.descriptionEarly soybean yield estimation has become a fundamental tool for market policy and food security. Considering a heterogeneous crop, this study investigates the spatial and spectral variability in soybean canopy reflectance to achieve grain yield estimation. Besides allowing crop mapping, remote sensing data also provide spectral evidence that can be used as a priori knowledge to guide sample collection for prediction models. In this context, this study proposes a sampling design method that distributes sample plots based on the spatial and spectral variability in vegetation spectral indices observed in the field. Random forest (RF) and multiple linear regression (MLR) approaches were applied to a set of spectral bands and six vegetation indices to assess their contributions to the soybean yield estimates. Experiments were conducted with a hyperspectral sensor of 25 contiguous spectral bands, ranging from 500 to 900 nm, carried by an unmanned aerial vehicle (UAV) to collect images during the R5 soybean growth stage. The tests showed that spectral indices specially designed from some bands could be adopted instead of using multiple bands with MLR. However, the best result was obtained with RF using spectral bands and the height attribute extracted from the photogrammetric height model. In this case, Pearson’s correlation coefficient was 0.91. The difference between the grain yield productivity estimated with the RF model and the weight collected at harvest was 1.5%, indicating high accuracy for yield prediction.-
Descrição: dc.descriptionDepartment of Cartography Faculty of Science and Technology São Paulo State University (UNESP)-
Descrição: dc.descriptionFaculty of Agronomy University of Western São Paulo (UNOESTE)-
Descrição: dc.descriptionFaculty of Agricultural Sciences and Technology São Paulo State University (UNESP)-
Descrição: dc.descriptionDepartment of Remote Sensing and Photogrammetry Finnish Geospatial Research Institute (FGI)-
Descrição: dc.descriptionDepartment of Cartography Faculty of Science and Technology São Paulo State University (UNESP)-
Descrição: dc.descriptionFaculty of Agricultural Sciences and Technology São Paulo State University (UNESP)-
Formato: dc.format3242-3260-
Idioma: dc.languageen-
Relação: dc.relationAgriEngineering-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectcanopy height model-
Palavras-chave: dc.subjectdata augmentation-
Palavras-chave: dc.subjectgrain yield productivity-
Palavras-chave: dc.subjectjudgement-based sampling design-
Palavras-chave: dc.subjectmultilinear regression-
Palavras-chave: dc.subjectrandom forest-
Título: dc.titleRemote Prediction of Soybean Yield Using UAV-Based Hyperspectral Imaging and Machine Learning Models-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.