Training Machine Learning Algorithms Using Remote Sensing and Topographic Indices for Corn Yield Prediction

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorAuburn University-
Autor(es): dc.contributorMacrypt R.G. Universidad de los Llanos-
Autor(es): dc.creatorOliveira, Mailson Freire de-
Autor(es): dc.creatorOrtiz, Brenda Valeska-
Autor(es): dc.creatorMorata, Guilherme Trimer-
Autor(es): dc.creatorJiménez, Andrés-F-
Autor(es): dc.creatorRolim, Glauco de Souza-
Autor(es): dc.creatorSilva, Rouverson Pereira da-
Data de aceite: dc.date.accessioned2025-08-21T21:18:05Z-
Data de disponibilização: dc.date.available2025-08-21T21:18:05Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/rs14236171-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249462-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249462-
Descrição: dc.descriptionMethods using remote sensing associated with artificial intelligence to forecast corn yield at the management zone level can help farmers understand the spatial variability of yield before harvesting. Here, spectral bands, topographic wetness index, and topographic position index were integrated to predict corn yield at the management zone using machine learning approaches (e.g., extremely randomized trees, gradient boosting machine, XGBoost algorithms, and stacked ensemble models). We tested four approaches: only spectral bands, spectral bands + topographic position index, spectral bands + topographic wetness index, and spectral bands + topographic position index + topographic wetness index. We also explored two approaches for model calibration: the whole-field approach and the site-specific model at the management zone level. The model’s performance was evaluated in terms of accuracy (mean absolute error) and tendency (estimated mean error). The results showed that it is possible to predict corn yield with reasonable accuracy using spectral crop information associated with the topographic wetness index and topographic position index during the flowering growth stage. Site-specific models increase the accuracy and reduce the tendency of corn yield forecasting on management zones with high, low, and intermediate yields.-
Descrição: dc.descriptionDepartment of Engineering and Mathematical Sciences São Paulo State University-
Descrição: dc.descriptionDepartment of Crop Soil and Environmental Sciences Auburn University-
Descrição: dc.descriptionDepartment of Mathematics and Physics Faculty of Basic Sciences and Engineering Macrypt R.G. Universidad de los Llanos-
Descrição: dc.descriptionDepartment of Engineering and Mathematical Sciences São Paulo State University-
Idioma: dc.languageen-
Relação: dc.relationRemote Sensing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectauto-machine learning-
Palavras-chave: dc.subjectdigital agriculture-
Palavras-chave: dc.subjectpredictive models-
Palavras-chave: dc.subjectsite-specific model-
Palavras-chave: dc.subjectZea maysL-
Título: dc.titleTraining Machine Learning Algorithms Using Remote Sensing and Topographic Indices for Corn Yield Prediction-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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