Automatic Detection of Whitefly Pest using Statistical Feature Extraction and Image Classification Methods

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Pest detection in plants and crops is essential for production of good quality food, improved quality of life and a stable agricultural economy. Excessive use of pesticides for pest control is harmful to plants, animals as well as human beings. Digital image processing along with computer vision techniques can be applied for early detection of pests and it can minimize amount of pesticides used in the plants. Generally, leaves are the most affected part of the plants. So, the study of interest is the leaf, rather than whole plant. Among many pests, the white fly is one of the most hazardous pests that affect the leaves. This paper presents an automated approach for detection of white fly pest from leaf images of various plants. Initially, image pre-processing techniques such as noise removal and contrast enhancement are used for improving the quality of image thus making it suitable for further processing. Then, k-means clustering method is used for segmenting pest from infected leaves. After that, texture features are extracted from those segmented images by statistical feature extraction methods such as Gray Level Run Length Matrix (GLRLM) and Gray Level Co-occurrence Matrix (GLCM). Finally, various classifiers like Support Vector machine, Artificial Neural Network, Bayesian classifier, Binary decision tree classifier and k-Nearest neighbor classifier are used to distinguish between healthy leaf images from white fly pest infected leaf images.