Classification of External Defects of Potato Tubers Using Convolutional Neural Network and Support Vector Machine

Abstract

To automate the grading of external defects of potato, two classification models (a convolutional neural network (CNN) and a support vector machine (SVM) model) were built based on either color or short-wave infrared (SWIR) images captured by different optical systems positioned along a grading line. The images were manually labeled into six external defect categories. Consequently, the highest classification accuracy (96.8 %) was obtained by the CNN model based on SWIR images. Furthermore, by visualizing influential regions for validation of the classification models, surface color and edge information features were apparent in the color images, while white highlighted areas with high reflectance were observed in the SWIR images.