Detection of Rice Leaf Diseases Using Image Processing

Diseases infected on plant leaves particularly in rice leaves are one of the significant issues faced by the farmers. As a result, it is extremely hard to deliver the quantity of food needed for the growing human population. Rice diseases have caused production and economic losses in the agricultural sector. It will like-wise influence the earnings of farmers who rely upon agriculture and nowadays farmers commit suicide because of misfortune experienced in agriculture. Detection of definite disease infected on plants will assist to plan various disease control procedures. Proposed method describes different strategies utilized for rice leaf disease classification purpose. Bacterial leaf blight, Leaf smut and Brown spot diseased images are segmented using Otsu's method. From the segmented area. various features are separated utilizing "Local Binary Patterns (LBP)" and "Histogram of Oriented Gradients (HOG)". Then the features are classified with the assistance of Support Vector Machine (SVM) and accomplished 94.6% with polynomial Kernel SVM and HOG.