Determination of application volume for coffee plantations using artificial neural networks and remote sensing

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 (R-2) 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.

Automated summary

Optimizing the application of phytosanitary products through the use of artificial intelligence and remote sensing techniques can lead to more sustainable agricultural practices. By utilizing remote sensing data and artificial neural networks (MLP), it is possible to estimate coffee tree volume accurately and apply pesticides at a variable rate.