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

Zeqing Yang, Zhimeng Li, Ning Hu (), Mingxuan Zhang, Wenbo Zhang, Lingxiao Gao, Xiangyan Ding, Zhengpan Qi and Shuyong Duan
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Zeqing Yang: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Zhimeng Li: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Ning Hu: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Mingxuan Zhang: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Wenbo Zhang: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Lingxiao Gao: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Xiangyan Ding: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Zhengpan Qi: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
Shuyong Duan: School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China

Agriculture, 2023, vol. 13, issue 2, 1-21

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 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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