Automatic multi-class classification of beetle pest using statistical feature extraction and support vector machine

Abstract

Plant diseases have turned into a problem as it can cause substantial decrease in both quality and quantity of agricultural harvests. Image processing can help in the following issues: early detection which leads to better growth of plant, and suggestion of the type and amount of pesticides knowing the pest. Leaves and stems are the most affected part of the plants. So, they are the study of interest. The beetle can affect the leaves, which leads to severe harm in the plant. In this paper, we propose an automated method for classification of various types of beetles, which consists of (i) image preprocessing techniques, such as contrast enhancement, are used to improve the quality of image which makes advance processing satisfactory; (ii) K-means clustering method is applied for segmenting pest from infected leaves; (iii) 24 features are extracted from those segmented images by using feature extraction, mainly GLCM; and (iv) support vector machine is used for multi-classification of the beetles. The proposed algorithm can successfully detect and classify the 12 classes of beetles with an accuracy of 89.17%, which outperforms the other multi-class pest-classification algorithm by a decent margin.