Experimental Evaluation of Coffee Leaf Disease Classification and Recognition Based on Machine Learning and Deep Learning Algorithms

Abstract

Coffee plant diseases constitute a significant danger to world coffee production, and the greatest challenge is to detect these diseases as early as possible to save the crop. Traditional methods are most often based on visual observations, often with errors in diagnosing diseases. Machine Learning has become a tool that presents itself as an alternative for automatically identifying plant diseases. Our study is to implement a robust method of classification and recognition of coffee leaf diseases using both classical ma learning and deep learning methods, so we set up a custom CNN. These methods were evaluated on the Arabica coffee leaf dataset known as JMuBEN. The results of the classical machine learning methods ranged from 81.03 to 100% and the best performance was obtained with SVM and Random Forest; while the deep learning. In comparison, these provided results between 97.37 and 100% with our CNN custom obtaining receiving accuracy with the lowest loss of 0.013%. Accuracy, precision score, recall, and MCC were employed as performance indicators to support this performance.