BRNeural ‑ Artificial Neural Networks Simulator with Topology Multilayer Perceptron Using the Encog Framework

The Artificial Neural Network (ANN) approach has had been applied in solutions for several problems such as classification, prediction and pattern recognition. Mostly, these problems belong to diversified areas of knowledge, not necessarily related to Computer Science, e.g. in agriculture, specifically in precision agriculture, recognition of diseases in plants, among others. Therefore, it is possible to visualize the multidisciplinary feature that ANNs have, motivating the development of tools for simulating more generic ANN, making its use possible by users whose knowledge of this type of methodology isn't deep. Inside this context, the just described work has had the goal of developing a tool that is part of the BRNeural research project, whose main goal was the development of a simulator named BRNeural (Brasil Neural) which is focused on the scientific and professional community since one of its proposals is to offer a generic environment to aid the creation, train and operate with ANNs. This tool consists in a module for training and testing ANNs with the Multilayer Perceptron (MLP) topology and it was developed using the ENCOG framework. For validating this module and all implemented training algorithms, the simulator was submitted to pattern recognition tests using agrometeorological data such as temperature, relative humidity, solar radiation, atmospheric pressure, wind direction, wind speed and temperature of leaf wetness, comparing the results obtained with another simulator which was developed using the MLP framework (Medeiros, 2003) with the specific purpose of classifying diseases in severity indexes. After validating, the simulator was submitted to other pattern recognition tests whose goal was the classification of diseases in the wheat like Blight, Spot blotch and Mildew in severity indexes through agrometeorological variables, showing its features such as output normalizations and the training algorithms Backpropagation, Resilient propagation and Quick propagation. The best results were obtained with the tests for the classification of diseases using severity indexes, 83.63% correct answers for Blight using the Quick propagation training algorithm and Equilateral normalization, 86.95% of correct answers for Mildew with Resilient Propagation algorithm and OneOf normalization and, for Spot blotch, 91.96% correct answers with Quick and Resilient Propagation algorithms, both using Equilateral normalization.