UAV imagery data and machine learning: a driving merger for predictive analysis of qualitative yield in sugarcane

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorBarbosa Júnior, Marcelo Rodrigues-
Autor(es): dc.creatorMoreira, Bruno Rafael de Almeida-
Autor(es): dc.creatorOliveira, Romário Porto de-
Autor(es): dc.creatorShiratsuchi, Luciano Shozo-
Autor(es): dc.creatorSilva, Rouverson Pereira-
Data de aceite: dc.date.accessioned2025-08-21T16:44:37Z-
Data de disponibilização: dc.date.available2025-08-21T16:44:37Z-
Data de envio: dc.date.issued2023-03-27-
Data de envio: dc.date.issued2023-03-27-
Data de envio: dc.date.issued2023-01-25-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/242674-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/242674-
Descrição: dc.descriptionPredicting sugarcane yield by quality allows stakeholders from research centers to industries to decide on the precise time and place to harvest a product on the field; hence, it can streamline workflow while leveling up the cost-effectiveness of full-scale production. °Brix and Purity can offer significant and reliable indicators of high-quality raw material for industrial processing for food and fuel. However, their analysis in a relevant laboratory can be costly, time-consuming, and not scalable. We, therefore, analyzed whether merging multispectral images and machine learning (ML) algorithms can develop a non-invasive, predictive framework to map canopy reflectance to °Brix and Purity. We acquired multispectral images data of a sugarcane-producing area via unmanned aerial vehicle (UAV) while determining °Brix and analytical Purity from juice in a routine laboratory. We then tested a suite of ML algorithms, namely multiple linear regression (MLR), random forest (RF), decision tree (DT), and support vector machine (SVM) for adequacy and complexity in predicting °Brix and Purity upon single spectral bands, vegetation indices (VIs), and growing degree days (GDD). We obtained evidence for biophysical functions accurately predicting °Brix and Purity. Those can bring at least 80% of adequacy to the modeling. Therefore, our study represents progress in assessing and monitoring sugarcane on an industrial scale. Our insights can offer stakeholders possibilities to develop prescriptive harvesting and resource-effective, high-performance manufacturing lines for by-products.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionVersão final do editor-
Descrição: dc.descriptionUniversidade Estadual Paulista (Unesp)-
Descrição: dc.descriptionCAPES: 001-
Formato: dc.formatapplication/pdf-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Publicador: dc.publisherFrontiers Media-
Relação: dc.relationFrontiers in Plant Science-
Direitos: dc.rightsinfo:eu-repo/semantics/openAccess-
Palavras-chave: dc.subjectRemote sensing-
Palavras-chave: dc.subjectSugarcane-
Palavras-chave: dc.subjectRipening-
Título: dc.titleUAV imagery data and machine learning: a driving merger for predictive analysis of qualitative yield in sugarcane-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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