Surface Gloss Evaluation of Apples Based on Computer Vision and Support Vector Machine Method

Abstract

This paper investigates computer vision applications for surface gloss evaluation to determine a quick surface gloss evaluation method for apples. “Red Fuji” apples were wax-coated with different concentrations of shellac solutions to obtain the apple samples with different levels of surface gloss. The surface gloss values and the color scales of the apple samples were detected using a pinhole gloss meter and a color meter. The apple sample images were captured and processed, and the color parameters of the high light areas were extracted. Support vector machine (SVM) regression and classification models were built to predict the surface gloss values and the surface gloss levels of apples, respectively. The results showed that to predict the surface gloss of apple samples, the correlation coefficients of the SVM regression model were 0.94 and 0.90 for the training and the testing groups, respectively. The classification accuracy rates of the SVM classification model for the training and the testing groups were 100 and 96.7%, respectively. Finally, apple surface gloss level classification software was developed, which showed good operating results for both classification accuracy rates and calculation speed. This paper provided a new surface gloss evaluation method based on computer vision for apples.