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.