This proposal focuses on the elaboration of a model that allows the early detection of the Xanthomonas Campestris disease by applying Machine Learning techniques, characterized by their high interpretability, improved by means of optimization algorithms, allowing to accurately identify the state of a plant (Healthy or diseased), so that farmers can take early action reducing the impact generated by the disease in the presentation and yield of the crop.
