Applying Machine Learning Techniques to Identify Damaged Potatoes

This paper examines the problem of detecting potatoes with mechanical damage using machine learning techniques. In this article, the authors proposed an algorithm for detecting damaged potato tubers on a conveyor belt that is characterized by speed and accuracy of recognition. The distinctive features of the algorithm are combining the methods of Viola-Jones and the convolutional networks, the application of two complementary classifiers, working in the usual gray color and inverted color. Also, the distinguishing feature is that the identified tubers are processed by the classifiers only once, regardless of the time in front of the video camera. The Viola-Jones method was used to identify individual tubers on the conveyor belt, and the convolutional networks were only used to recognize damaged tubers. Moreover, two complementary networks were used for classification, one of which worked in gray gradation and the other in inverted color. The algorithm was implemented using the OpenCV library in Python. Testing was carried out in conditions close to the conditions of potato storage at vegetable bases. The percentage of properly-recognized damaged tubers was 92,1%.