Maize Leaf Disease Detection and Classification Using Machine Learning Algorithms

Abstract

Plant diseases are the major cause of low agricultural productivity. Mostly the farmers encounter difficulties in controlling and detecting the plant diseases. Thus, early detection of these diseases will be beneficial for farmers to avoid further losses. This paper focuses on supervised machine learning techniques such as Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) for maize plant disease detection with the help of the images of the plant. The aforesaid classification techniques are analyzed and compared in order to select the best suitable model with the highest accuracy for plant disease prediction. The RF algorithm results with the highest accuracy of 79.23% as compared to the rest of the classification techniques. All the aforesaid trained models will be used by the farmers for the early detection and classification of the new image diseases as a preventive measure.