Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/48684
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Campo DCValorIdioma
dc.creatorOliveira, Mailson Freire de-
dc.creatorSantos, Adão Felipe dos-
dc.creatorKazama, Elizabeth Haruna-
dc.creatorRolim, Glauco de Souza-
dc.creatorSilva, Rouverson Pereira da-
dc.date.accessioned2021-12-14T20:32:06Z-
dc.date.available2021-12-14T20:32:06Z-
dc.date.issued2021-05-
dc.identifier.citationOLIVEIRA, 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.urihttps://doi.org/10.1016/j.compag.2021.106096pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/48684-
dc.description.abstractMethods 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.languageenpt_BR
dc.publisherElsevierpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceComputers and Electronics in Agriculturept_BR
dc.subjectCoffee canopypt_BR
dc.subjectVegetation indexpt_BR
dc.subjectVariable rate sprayingpt_BR
dc.subjectMachine learningpt_BR
dc.subjectDigital agriculturept_BR
dc.subjectRedes neurais artificiaispt_BR
dc.subjectSensoriamento remotopt_BR
dc.subjectCafeiculturapt_BR
dc.subjectÍndice de vegetaçãopt_BR
dc.subjectPulverização - Taxa variávelpt_BR
dc.subjectAprendizado de máquinapt_BR
dc.subjectAgricultura digitalpt_BR
dc.titleDetermination of application volume for coffee plantations using artificial neural networks and remote sensingpt_BR
dc.typeArtigopt_BR
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