Rice Disease Classification Using Supervised Machine Learning Approach

Rice is an important concern for the human beings. Most of the people depend on rice for their survival in the society. Rice diseases provide adverse effect which leads to unnecessary wastage of rice. So, it is very much essential for the classification of rice diseases at the earliest so that preventive measures can be taken accordingly. Machine Learning (ML) based methods have a greater role for the classification, identification and analysis of different images for several applications in the current scenario. In this paper, an attempt has been made for the classification of rice diseases such as BrownSpot, Hispa and LeafBlast and the Healthy cases from the analysis of different rice disease images using supervised ML based methods. In this work, the ML based methods such as k-Nearest Neighbor (k-NN), neural network (NN), Support Vector Machine (SVM), decision tree (DT), random forest (RF), Logistic Regression (LR), AdaBoost (AB) and Naïve Bayes (NB) are used for such classification. The methods are evaluated in terms of Classification Accuracy (CA) performance metric. This work is carried out using Orange 3.26.0.