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Campo DC | Valor | Idioma |
---|---|---|
dc.creator | Oliveira, Mailson Freire de | - |
dc.creator | Santos, Adão Felipe dos | - |
dc.creator | Kazama, Elizabeth Haruna | - |
dc.creator | Rolim, Glauco de Souza | - |
dc.creator | Silva, Rouverson Pereira da | - |
dc.date.accessioned | 2021-12-14T20:32:06Z | - |
dc.date.available | 2021-12-14T20:32:06Z | - |
dc.date.issued | 2021-05 | - |
dc.identifier.citation | OLIVEIRA, M. F. de et al. Determination of application volume for coffee plantations using artificial neural networks and remote sensing. Computers and Electronics in Agriculture, [S. I.], v. 184, May 2021. DOI: https://doi.org/10.1016/j.compag.2021.106096. | pt_BR |
dc.identifier.uri | https://doi.org/10.1016/j.compag.2021.106096 | pt_BR |
dc.identifier.uri | http://repositorio.ufla.br/jspui/handle/1/48684 | - |
dc.description.abstract | Methods for optimizing the application of phytosanitary products can be an alternative for sustainable agriculture. Such methods can be achieved with the use of artificial intelligence and remote sensing techniques. Our experiments were carried out in a commercial coffee plantation, where morphological variables (height and diameter) and vegetation indexes (normalized difference vegetation index, NDVI and normalized difference red edge, NDRE) were collected in the upper, medium, and lower thirds of the coffee plant. From the remote sensing data, experiments were developed to determine the best neural network topology, in terms of accuracy (RMSE) and precision (R2) and type (Multilayer Perceptron “MLP” and Radial Basis Function “RBF”), to estimate morphological variables. From these results, we evaluated the possibility of applying pesticides at a variable rate, using the tree row volume principle. The results show that, using remote sensing and artificial neural networks (MLP), it is possible to estimate coffee tree volume with reasonable accuracy. This can be done using a multi-layer perceptron model to estimate coffee tree height and diameter using vegetation indexes of different parts of the plant as input. | pt_BR |
dc.language | en | pt_BR |
dc.publisher | Elsevier | pt_BR |
dc.rights | restrictAccess | pt_BR |
dc.source | Computers and Electronics in Agriculture | pt_BR |
dc.subject | Coffee canopy | pt_BR |
dc.subject | Vegetation index | pt_BR |
dc.subject | Variable rate spraying | pt_BR |
dc.subject | Machine learning | pt_BR |
dc.subject | Digital agriculture | pt_BR |
dc.subject | Redes neurais artificiais | pt_BR |
dc.subject | Sensoriamento remoto | pt_BR |
dc.subject | Cafeicultura | pt_BR |
dc.subject | Índice de vegetação | pt_BR |
dc.subject | Pulverização - Taxa variável | pt_BR |
dc.subject | Aprendizado de máquina | pt_BR |
dc.subject | Agricultura digital | pt_BR |
dc.title | Determination of application volume for coffee plantations using artificial neural networks and remote sensing | pt_BR |
dc.type | Artigo | pt_BR |
Aparece nas coleções: | DAG - Artigos publicados em periódicos |
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