文献类型： 外文期刊

作者： Guan Zhi-bin ¹ ; Zhang Yan-qi ¹ ; Chai Xiu-juan ¹ ; Chai Xin ¹ ; Zhang Ning ¹ ; Zhang Jian-hua ¹ ; Sun Tan ² ;

作者机构： 1.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Agr Big Data, Beijing 100081, Peoples R China

3.Chinese Acad Agr Sci, Natl Nanfan Res Inst Sanya, Sanya 572024, Peoples R China

关键词： GCN; multi-view; fine-grained; visual feature; appearance; diameter size

期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：4.8； 五年影响因子：4.8 ）

ISSN： 2095-3119

年卷期： 2023 年 22 卷 1 期

页码：

收录情况： SCI

摘要： 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.