Dry mass grassland estimation using UAV ultra-wide RGB images

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorCzech University of Life Sciences Prague-
Autor(es): dc.contributorUniversity of Western São Paulo (UNOESTE)-
Autor(es): dc.creatorda Silva, Rebeca Campos Emiliano-
Autor(es): dc.creatorTommaselli, Antonio Maria Garcia-
Autor(es): dc.creatorImai, Nilton Nobuhiro-
Autor(es): dc.creatorMartins-Neto, Rorai Pereira-
Autor(es): dc.creatorda Silva da Silveira, Daniel-
Autor(es): dc.creatorMoro, Edemar-
Data de aceite: dc.date.accessioned2025-08-21T21:28:36Z-
Data de disponibilização: dc.date.available2025-08-21T21:28:36Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-11-03-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.5194/isprs-annals-X-3-2024-69-2024-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308930-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308930-
Descrição: dc.descriptionDry mass is an important parameter to optimise grassland management. Traditionally, dry mass values are estimated manually by cutting, drying, and weighing vegetation samples. In large areas of cultivation, this becomes a time-consuming and costly activity. In recent years, many researchers have studied different sensors embedded in Unmanned Aerial Vehicles (UAV) to collect spatial data and estimate biomass using machine learning algorithms for forest and agricultural applications. However, there needs to be more research dealing with estimating production indices for pasture, especially in Brazil, as stated. This study evaluates the feasibility of using the GoPro wide-angle RGB camera on UAVs (Unmanned Aerial Vehicles) to estimate the dry mass of pastures. Different data analysis methods were compared, including the combination of vegetation indices (VIs) values and three-dimensional metrics (3D) extracted from the Canopy Height Model (CHM): all metrics (ALL), three VIs plus four 3D metrics (VI3 + CHM4) and only 3D metrics. Random Forest (RF) machine learning algorithm was used to estimate dry mass. The best results were obtained when merging all the variables from the two flight campaigns, with a coefficient of determination (R2) of 0.80 for the model and a Pearson Correlation Coefficient (PCC) of 0.85 for validation, with a Root Mean Square Error (RMSE%) of 20.5%. In summary, using RGB sensors embedded in UAVs is a promising technique for estimating farm grazing parameters.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionDepartment of Cartography São Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionFaculty of Forestry and Wood Sciences Czech University of Life Sciences Prague, Kamycka 129-
Descrição: dc.descriptionUniversity of Western São Paulo (UNOESTE), São Paulo-
Descrição: dc.descriptionDepartment of Cartography São Paulo State University (UNESP), São Paulo-
Descrição: dc.descriptionCNPq: 130411/2022-1-
Descrição: dc.descriptionFAPESP: 2021/06029-7-
Descrição: dc.descriptionCNPq: 303670_2018-5-
Descrição: dc.descriptionCAPES: 88887.310313/2018-00-
Descrição: dc.descriptionCAPES: 88887.898553/2023-00-
Formato: dc.format69-75-
Idioma: dc.languageen-
Relação: dc.relationISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectdry matter-
Palavras-chave: dc.subjectgrassland-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjectprecision agriculture-
Palavras-chave: dc.subjectremote sensing-
Palavras-chave: dc.subjectUAV-
Título: dc.titleDry mass grassland estimation using UAV ultra-wide RGB images-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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