Modelo de clasificación supervisado para la detección de la Roya en hojas del cultivo de café aplicando técnicas de Machine Learning

Abstract

This proposal focuses on the development of a model that allows the early detection of the Rust disease and its different stages presented in the leaves of the coffee crop, applying Machine Learning techniques characterized by their interpretability, improved with respect to their level of accuracy through hybrid optimization algorithms, providing an alternative to traditional algorithms and models of image analysis. Therefore, the model will stand out for its high performance in the analysis and classification of images, identifying the state of a plant (healthy or diseased), allowing farmers to take corrective measures in a timely manner, reducing the impact generated by the disease on crop yield.