Potato Skin Defect Detection and Classification through Image Processing

Upon progression of how to detect a quality of a potato whether it may be a normal or a diseased potato. Potatoes are a tuber crop that populated 60% of the total crop production. Having potatoes that are green on its skin or even thumbnail marks on it is a sign of it not being healthy to consume. This paper presents a research and development to the quality detection of a potato with image processing and support vector machine. The prototype should be able to detect the skin of a potato if it has thumbnail marks and green skin with the support of Microcomputer Raspberry Pi 4 B and an integrated Raspberry Pi Camera 1.3 for image processing and machine learning. With Python as a language used in the creation of the system a series of function such as OpenCV, SKLearn, Image Acquisition, Image processing, Canny Edge, Gaussian Blur, area, and length plotting, and SVM. Fifteen samples of varying potatoes from green skin, thumbnail crack, and normal to know the effectiveness of the design giving a total of ninety photos per sample that gives a result of 96.67% detection accuracy. Hence the system is an effective method on detecting the said defects on the skin of the potato.