Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal de Mato Grosso do Sul (UFMS)-
Autor(es): dc.contributorUniv Western Sao Paulo-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniv Waterloo-
Autor(es): dc.contributorSanta Catarina State Univ UDESC-
Autor(es): dc.creatorOsco, Lucas Prado-
Autor(es): dc.creatorMarques Ramos, Ana Paula-
Autor(es): dc.creatorPereira, Danilo Roberto-
Autor(es): dc.creatorSaito Moriya, Erika Akemi [UNESP]-
Autor(es): dc.creatorImai, Nilton Nobuhiro [UNESP]-
Autor(es): dc.creatorMatsubara, Edson Takashi-
Autor(es): dc.creatorEstrabis, Nayara-
Autor(es): dc.creatorSouza, Mauricio de-
Autor(es): dc.creatorMarcato Junior, Jose-
Autor(es): dc.creatorGoncalves, Wesley Nunes-
Autor(es): dc.creatorLi, Jonathan-
Autor(es): dc.creatorLiesenberg, Veraldo-
Autor(es): dc.creatorCreste, Jose Eduardo-
Data de aceite: dc.date.accessioned2022-02-22T00:09:35Z-
Data de disponibilização: dc.date.available2022-02-22T00:09:35Z-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2020-12-09-
Data de envio: dc.date.issued2019-12-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/rs11242925-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/196489-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/196489-
Descrição: dc.descriptionThe traditional method of measuring nitrogen content in plants is a time-consuming and labor-intensive task. Spectral vegetation indices extracted from unmanned aerial vehicle (UAV) images and machine learning algorithms have been proved effective in assisting nutritional analysis in plants. Still, this analysis has not considered the combination of spectral indices and machine learning algorithms to predict nitrogen in tree-canopy structures. This paper proposes a new framework to infer the nitrogen content in citrus-tree at a canopy-level using spectral vegetation indices processed with the random forest algorithm. A total of 33 spectral indices were estimated from multispectral images acquired with a UAV-based sensor. Leaf samples were gathered from different planting-fields and the leaf nitrogen content (LNC) was measured in the laboratory, and later converted into the canopy nitrogen content (CNC). To evaluate the robustness of the proposed framework, we compared it with other machine learning algorithms. We used 33,600 citrus trees to evaluate the performance of the machine learning models. The random forest algorithm had higher performance in predicting CNC than all models tested, reaching an R-2 of 0.90, MAE of 0.341 gkg(-1) and MSE of 0.307 gkg(-1). We demonstrated that our approach is able to reduce the need for chemical analysis of the leaf tissue and optimizes citrus orchard CNC monitoring.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionFAPESC-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionUniv Fed Mato Grosso do Sul, Fac Engn Architecture & Urbanism & Geog, Ave Costa E Silva, BR-79070900 Campo Grande, MS, Brazil-
Descrição: dc.descriptionUniv Western Sao Paulo, Environm & Reg Dev, R Jose Bongiovani,700-Cidade Univ, BR-19050920 Presidente Prudente, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Dept Cartog Sci, BR-19060900 Presidente Prudente, Brazil-
Descrição: dc.descriptionUniv Fed Mato Grosso do Sul, Fac Comp Sci, Ave Costa E Silva, BR-79070900 Campo Grande, MS, Brazil-
Descrição: dc.descriptionUniv Waterloo, Dept Geog & Environm Management, Waterloo, ON N2L 3G1, Canada-
Descrição: dc.descriptionUniv Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada-
Descrição: dc.descriptionSanta Catarina State Univ UDESC, Forest Engn Dept, Ave Luiz de Camoes 2090, BR-88520000 Conta Dinheiro, SC, Brazil-
Descrição: dc.descriptionUniv Western Sao Paulo, Agron Dev, R Jose Bongiovani,700 Cidade Univ, BR-19050920 Presidente Prudente, Brazil-
Descrição: dc.descriptionSao Paulo State Univ, Dept Cartog Sci, BR-19060900 Presidente Prudente, Brazil-
Descrição: dc.descriptionCAPES: p: 88881.311850/2018-01-
Descrição: dc.descriptionFAPESC: 2017TR1762-
Descrição: dc.descriptionCNPq: 313887/2018-7-
Formato: dc.format17-
Idioma: dc.languageen-
Publicador: dc.publisherMdpi-
Relação: dc.relationRemote Sensing-
???dc.source???: dc.sourceWeb of Science-
Palavras-chave: dc.subjectUAV multispectral imagery-
Palavras-chave: dc.subjectspectral vegetation indices-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectplant nutrition-
Título: dc.titlePredicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.