Fuzzy Inference Systems Optimization Techniques for Detecting Xanthomonas campestris Disease

This chapter shows the outcomes for four optimization models based on fuzzy inference systems, intervened using Quasi-Newton and genetic algorithms, to early assess bean plants’ leaves for Xanthomonas campestris disease. Plant diseases are an important threat to food production. While major pathogenicity determinants required for disease have been extensively studied, less is known on how pathogens thrive during host colonization, especially at early infection stages. This research is classified as experimental and it is focused on the provided dataset analysis to establish the critical variables, and the most relevant tools for pattern and behavior recognition. The RGB colour intensity for the data sets and photographs used to analyse the model implementation defines the classification of the plant's condition (healthy or insanity). The best model performance is 99.68% when compared with the training data and a 94% effectiveness rate on the detection of Xanthomonas campestris in a bean leave image. One of the most important characteristics of neural networks is their high accuracy to the cost of interpretability, nevertheless, the best model for this research developed using a fuzzy inference system does not sacrifice interpretability. Therefore, these results would allow farmers to take early measures to reduce the impact of the disease on the look and performance of green bean crops. The model has high accuracy and interpretability when optimized and a greater capacity to detect the existence of the disease in a plant.