A real-time mathematical computer method for potato inspection using machine vision

Detection of external defects on potatoes is the most important technology in the realization of automatic potato sorting stations. This paper presents a hierarchical grading method applied to the potatoes. In this work a potato defect detection combining with size sorting system using the machine vision will be proposed. This work also will focus on the mathematics methods used in automation with a particular emphasis on the issues associated with designing, implementing and using classification algorithms to solve equations. In the first step, a simple size sorting based on mathematical binarization is described, and the second step is to segment the defects; to do this, color based classifiers are used. All the detection standards for this work are referenced from the United States Agriculture Department, and Canadian Food Industries. Results show that we have a high accuracy in both size sorting and classification. Experimental results show that support vector machines have very high accuracy and speed between classifiers for defect detection.