Using sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage mass, crude protein, and fiber content

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorSigfarm Intelligence LLC-
Autor(es): dc.contributorTexas A & amp;M University-
Autor(es): dc.creatorFernandes, Marcia Helena Machado da Rocha-
Autor(es): dc.creatorFernandesJunior, Jalme de Souza-
Autor(es): dc.creatorAdams, Jordan Melissa-
Autor(es): dc.creatorLee, Mingyung-
Autor(es): dc.creatorReis, Ricardo Andrade-
Autor(es): dc.creatorTedeschi, Luis Orlindo-
Data de aceite: dc.date.accessioned2025-08-21T18:20:48Z-
Data de disponibilização: dc.date.available2025-08-21T18:20:48Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1038/s41598-024-59160-x-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/300530-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/300530-
Descrição: dc.descriptionGrasslands cover approximately 24% of the Earth’s surface and are the main feed source for cattle and other ruminants. Sustainable and efficient grazing systems require regular monitoring of the quantity and nutritive value of pastures. This study demonstrates the potential of estimating pasture leaf forage mass (FM), crude protein (CP) and fiber content of tropical pastures using Sentinel-2 satellite images and machine learning algorithms. Field datasets and satellite images were assessed from an experimental area of Marandu palisade grass (Urochloa brizantha sny. Brachiaria brizantha) pastures, with or without nitrogen fertilization, and managed under continuous stocking during the pasture growing season from 2016 to 2020. Models based on support vector regression (SVR) and random forest (RF) machine-learning algorithms were developed using meteorological data, spectral reflectance, and vegetation indices (VI) as input features. In general, SVR slightly outperformed the RF models. The best predictive models to estimate FM were those with VI combined with meteorological data. For CP and fiber content, the best predictions were achieved using a combination of spectral bands and meteorological data, resulting in R2 of 0.66 and 0.57, and RMSPE of 0.03 and 0.04 g/g dry matter. Our results have promising potential to improve precision feeding technologies and decision support tools for efficient grazing management.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionDepartment of Animal Science Sao Paulo State University (UNESP), Campus Jaboticabal-
Descrição: dc.descriptionSigfarm Intelligence LLC-
Descrição: dc.descriptionDepartment of Animal Science Texas A & amp;M University-
Descrição: dc.descriptionDepartment of Animal Science Sao Paulo State University (UNESP), Campus Jaboticabal-
Descrição: dc.descriptionFAPESP: 2015/16631-5-
Descrição: dc.descriptionFAPESP: 2017/18750-7-
Idioma: dc.languageen-
Relação: dc.relationScientific Reports-
???dc.source???: dc.sourceScopus-
Título: dc.titleUsing sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage mass, crude protein, and fiber content-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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