Predicting Sugarcane Biometric Parameters by UAV Multispectral Images and Machine Learning

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorde Oliveira, Romário Porto-
Autor(es): dc.creatorBarbosa Júnior, Marcelo Rodrigues-
Autor(es): dc.creatorPinto, Antônio Alves-
Autor(es): dc.creatorOliveira, Jean Lucas Pereira-
Autor(es): dc.creatorZerbato, Cristiano-
Autor(es): dc.creatorFurlani, Carlos Eduardo Angeli-
Data de aceite: dc.date.accessioned2025-08-21T20:23:23Z-
Data de disponibilização: dc.date.available2025-08-21T20:23:23Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-09-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/agronomy12091992-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/249178-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/249178-
Descrição: dc.descriptionMultispectral sensors onboard unmanned aerial vehicles (UAV) have proven accurate and fast to predict sugarcane yield. However, challenges to a reliable approach still exist. In this study, we propose to predict sugarcane biometric parameters by using machine learning (ML) algorithms and multitemporal data through the analysis of multispectral images from UAV onboard sensors. The research was conducted on five varieties of sugarcane, as a way to make a robust approach. Multispectral images were collected every 40 days and the evaluated biometric parameters were: number of tillers (NT), plant height (PH), and stalk diameter (SD). Two ML models were used: multiple linear regression (MLR) and random forest (RF). The results showed that models for predicting sugarcane NT, PH, and SD using time series and ML algorithms had accurate and precise predictions. Blue, Green, and NIR spectral bands provided the best performance in predicting sugarcane biometric attributes. These findings expand the possibilities for using multispectral UAV imagery in predicting sugarcane yield, particularly by including biophysical parameters.-
Descrição: dc.descriptionDepartment of Engineering and Exact Sciences School of Veterinarian and Agricultural Sciences São Paulo State University (Unesp), SP-
Descrição: dc.descriptionDepartment of Engineering and Exact Sciences School of Veterinarian and Agricultural Sciences São Paulo State University (Unesp), SP-
Idioma: dc.languageen-
Relação: dc.relationAgronomy-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectdigital agriculture-
Palavras-chave: dc.subjectnumber of tillers-
Palavras-chave: dc.subjectplant height-
Palavras-chave: dc.subjectspectral bands-
Palavras-chave: dc.subjectstalk diameter-
Título: dc.titlePredicting Sugarcane Biometric Parameters by UAV Multispectral Images and Machine Learning-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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