Proposed Grade Discrimination Model Combining Classification and Grade Regression

Abstract

Image processing techniques based on neural networks enable highly accurate discrimination in fruit and vegetable sorting. However, conventional classification models may not be sufficiently accurate for grading based on fine features, such as in the simultaneous output of high estimated probabilities for multiple distant grades. Here we propose a classification model combining conventional classification and grade regression for grade discrimination and verified its effectiveness by using the grade discrimination of onion peelings as a test case. The model reduced misclassification to distant grades without decreasing discrimination accuracy, relative to conventional classification and regression models.