Convolutional neural networks in predicting cotton yield from images of commercial fields

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorTedesco-Oliveira, Danilo [UNESP]-
Autor(es): dc.creatorPereira da Silva, Rouverson [UNESP]-
Autor(es): dc.creatorMaldonado, Walter [UNESP]-
Autor(es): dc.creatorZerbato, Cristiano [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:33:56Z-
Data de disponibilização: dc.date.available2022-02-22T00:33:56Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.compag.2020.105307-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/201593-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/201593-
Descrição: dc.descriptionOne way to improve the quality of mechanized cotton harvesting is to change harvester settings and adjustments throughout the process, according to information obtained during the operation. We believe that yield predictions are important for managing the quality of operation, aiming at increasing efficiency and reducing losses. Therefore, this study aimed to develop an automated system for cotton yield prediction from color images acquired by a simple mobile device. We propose a robust approach to environmental conditions, training detection algorithms with images acquired at different times throughout the day, and evaluating three different scenarios (low-, average-, and high-demand computational resources). The experimental results for the average demand computational scenario, which are suitable for real-time deployment on low-cost devices such as smartphones and other ARM-processed devices, indicated the possibility of counting bolls using images acquired at different times throughout the day, with mean errors of 8.84% (∼5 bolls). Furthermore, we observed a 17.86% error when predicting yield using 205 images from the testing dataset, which is equivalent to about 19.14 g.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionSão Paulo State University School of Agricultural and Veterinary Sciences (UNESP/FCAV)-
Descrição: dc.descriptionSão Paulo State University School of Agricultural and Veterinary Sciences (UNESP/FCAV)-
Idioma: dc.languageen-
Relação: dc.relationComputers and Electronics in Agriculture-
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Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectObject detection-
Palavras-chave: dc.subjectSmart harvesting-
Palavras-chave: dc.subjectYield prediction-
Título: dc.titleConvolutional neural networks in predicting cotton yield from images of commercial fields-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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