Paddy Leaf Disease Detection Using Machine Learning Technique

Abstract

One of the most important key resources to prevent global warming on the planet is plants. But the plants are suffering from various diseases. In recent time, research has been begun for acknowledgement of plant disease. Paddy disease detection is the key intention of this paper. Brown Spot Disease (BSD), Leaf Blast Disease (BD), and Leaf Blight Disease (LBD) are a few of the paddy diseases that prevent the paddy from growing and protecting every portion of the plant including diseases that can affect paddy at various stage of growth. This research examined 3 different disease kinds as well as one group of healthy paddy leaves. Bacteria, fungi, and other organisms are among those that can cause paddy disease. The Technique was created to eliminate noise automatically by decreasing the time needed to measure the impact of paddy leaf disease on humans so using machine learning techniques k-means for image segmentation and an automated detection method to get the best results for finding paddy leaf disease with the approach of machine learning using classifications with the best accuracy. To measure classification of this paper K-Fold cross validation techniques has been used. Applying 4 classes of paddy leaf’s into Random Forest, Decision Tree, Logistic Regression and SVM like support vector classifier (SVC), among then Random Forest gave the highest 94.16% accuracy with the using of K-fold cross validation techniques in predicting the three classes of paddy leaf disease with one group of healthy paddy leaves.