Multi-Index Grading Method for Pear Appearance Quality Based on Machine Vision

Abstract

The appearance quality of fruits affects consumers’ judgment of their value, and grading the quality of fruits is an effective means to improve their added value. The purpose of this study is to transform the grading of pear appearance quality into the classification of the categories under several quality indexes based on industry standards and design effective distinguishing features for training the classifier. The grading of pear appearance quality is transformed into the classification of pear shapes, surface colors and defects. The symmetry feature and quasi-rectangle feature were designed and the back propagation (BP) neural network was trained to distinguish standard shape, apical shape and eccentric shape. The mean and variance features of R and G channels were used to train support vector machine (SVM) to distinguish standard color and deviant color. The surface defect area was used to participate in pear appearance quality classification and the gray level co-occurrence matrix (GLCM) features of defect area were extracted to train BP neural network to distinguish four common defect categories: tabbed defects, bruised defects, abraded defects and rusty defects. The accuracy rates of the above three classifiers reached 83.3%, 91.0% and 76.6% respectively, and the accuracy rate of pear appearance quality grading based on grading rules was 80.5%. In addition, the hardware system prototype for experimental purpose was designed, which have certain reference significance for the further construction of the pear appearance quality grading pipeline.

Keywords

• pear grading
• multi-index grading
• feature extraction
• machine vision