Improved Rice Leaf Disease Detection using Fusion of Otsu Thresholding and Thepade SBTC Features

Abstract

Rice leaf disease has many morphs, including Rice Blast Healthy Leaves, Sheath Blight, Sheath Rot, Hispa, Blade Blast, and Leaf Smut, among others. One of the important difficulties that farmers are concerned about is detecting the type of polluted illnesses in rice leaves. Identifying symptoms and understanding this class of disorders are crucial functions of The International Rice Research Institute (IRRI). IRRI is an organization whose mission is to reduce poverty and hunger among the populations and individuals who depend on rice-based agri-food systems. The proposed technique in the paper does rice leaf disease identification using the fusion of features generated with Thepade sorted block truncation coding (Thepade SBTC) and Otsu thresholding. These features are utilized for training the machine learning algorithms alias Simple Logistic Regression (SLR), Random Tree (RT), J48, Random Forest (RF), BayesNet, Naive Bayes, and an ensemble of the algorithms. The experimental validation is done using a rice leaf dataset having four categories alias Healthy, Brown Spot, Hispa, and Leaf Blast. The accuracy of rice leaf disease identification is used as the performance metric. The proposed feature fusion has given better accuracy of rice leaf disease identification. The ensemble of machine learning algorithms (with majority voting) has shown better rice leaf disease identification.