Visual learning graph convolution for multi-grained orange quality grading

Abstract

The quality of oranges is grounded on their appearance and diameter. Appearance refers to the skin’s smoothness and surface cleanliness; diameter refers to the transverse diameter size. They are visual attributes that visual perception technologies can automatically identify. Nonetheless, the current orange quality assessment needs to address two issues: 1) There are no image datasets for orange quality grading; 2) It is challenging to effectively learn the fine-grained and distinct visual semantics of oranges from diverse angles. This study collected 12 522 images from 2 087 oranges for multi-grained grading tasks. In addition, it presented a visual learning graph convolution approach for multi-grained orange quality grading, including a backbone network and a graph convolutional network (GCN). The backbone network’s object detection, data augmentation, and feature extraction can remove extraneous visual information. GCN was utilized to learn the topological semantics of orange feature maps. Finally, evaluation results proved that the recognition accuracy of diameter size, appearance, and fine-grained orange quality were 99.50, 97.27, and 97.99%, respectively, indicating that the proposed approach is superior to others.

Keywords

• GCN
• multi-view
• fine-grained
• visual feature
• appearance
• diameter size