Application of Support Vector Machine to Detect Microbial Spoilage of Mushrooms

One of the main important parts in the robot vision system of the mushroom harvesting robot is to detect mushroom damage either caused by microbial or mechanical origin. Mushrooms must be classified as healthy or unhealthy to ensure proper handling and maximize crop yield. To solve the problem of identification, a fast and non-destructive method, Support Vector Machine (SVM), is applied to improve the recognition accuracy and efficiency of the robot. Initially, a median filter is applied to remove the inherent noise in the colored image. SIFT features of the image are then extracted and computed forming a vector, which is then quantized into visual words. Finally, the histogram of the frequency of each element in the visual vocabulary is created and fed into an SVM classifier, which categorizes the mushrooms as either healthy or unhealthy. Our preliminary results for mushroom classification are promising and the experiments carried out on the data set highlight faster computation time and a higher rate of accuracy, reaching over 90% using this method, which can be employed in real life scenario.