Leaf image based cucumber disease recognition using sparse representation classification

•The log frequency histogram features are extracted from each lesion image.•The lesion shape features are combined with color features.•The sparse coefficients can explain the importance of each disease leaf image.•The cucumber disease is recognized by SRC.

Most existing image-based crop disease recognition algorithms rely on extracting various kinds of features from leaf images of diseased plants. They have a common limitation as the features selected for discriminating leaf images are usually treated as equally important in the classification process. We propose a novel cucumber disease recognition approach which consists of three pipelined procedures: segmenting diseased leaf images by K-means clustering, extracting shape and color features from lesion information, and classifying diseased leaf images using sparse representation (SR). A major advantage of this approach is that the classification in the SR space is able to effectively reduce the computation cost and improve the recognition performance. We perform a comparison with four other feature extraction based methods using a leaf image dataset on cucumber diseases. The proposed approach is shown to be effective in recognizing seven major cucumber diseases with an overall recognition rate of 85.7%, higher than those of the other methods.