Analysis of content based image retrieval for plant leaf diseases using color, shape and texture features

This research paper is an attempt to present Content Based Image Retrieval (CBIR) system developed for retrieving diseased leaves of soybean. It uses color, shape and texture features of leaf. Color features are extracted using HSV color histogram. Scale Invariant Feature Transform (SIFT) provides shape features in the form of matching key points. Local Binary Pattern (LBP) and Gabor filter are widely used texture features. Novel texture feature named Local Gray Gabor Pattern (LGGP) is proposed by combining LBP and Gabor. Performance of all these features with respect to retrieval precision is tested for three soybean leaf diseases. Further color, shape and texture features are combined to increase performance. It is found that when LGGP is combined with color histogram and SIFT retrieval precision is improved. Retrieval efficiency of about 96%, 68% and 76% is achieved for soybean leaves affected by mosaic virus, septoria brown spot and pod mottle disease respectively. Average retrieval efficiency of 80% (for the top 5 retrieval) and 72% (for the top 10 retrieval) is obtained by combined features. This retrieval precision is database dependent and varies with size of the database and quality of images.