Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/48684
Title: Determination of application volume for coffee plantations using artificial neural networks and remote sensing
Keywords: Coffee canopy
Vegetation index
Variable rate spraying
Machine learning
Digital agriculture
Redes neurais artificiais
Sensoriamento remoto
Cafeicultura
Índice de vegetação
Pulverização - Taxa variável
Aprendizado de máquina
Agricultura digital
Issue Date: May-2021
Publisher: Elsevier
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.
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.
URI: https://doi.org/10.1016/j.compag.2021.106096
http://repositorio.ufla.br/jspui/handle/1/48684
Appears in Collections:DAG - Artigos publicados em periódicos

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