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| Visual learning graph convolution for multi-grained orange quality grading |
| GUAN Zhi-bin1, ZHANG Yan-qi1, CHAI Xiu-juan1, CHAI Xin1, ZHANG Ning1, 2, ZHANG Jian-hua1, 3, SUN Tan2, 3 |
1 Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
2 Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, P.R.China
3 National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, P.R.China
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摘要
柑橘品质取决于其外观优劣和横径大小。外观指果皮的光滑度和清洁度；横径指柑橘的横向直径大小。柑橘的外观和横径均属于视觉属性特征，能够被视觉感知技术自动识别。然而，目前柑橘品质分级任务中有两个问题亟待解决：1）缺少可用于柑橘品质分级的图像数据集；2）从多角度有效地学习柑橘的细粒度和差异化视觉语义具有较高挑战性。对于多粒度品质分级任务，我们收集了源于2087个柑橘的12522张图像；此外，我们提出了一种用于多粒度柑橘品质分级的视觉学习图卷积方法，包括骨干网络和图卷积网络。骨干网络中的目标检测、数据增强和特征提取能够剔除柑橘图像中无关的视觉信息；图卷积网络被用于学习柑橘特征映射的拓扑语义。最后，评估结果显示横径大小、外观和
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.
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Received: 01 May 2021
Accepted: 06 September 2022
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This work was supported by the National Natural Science Foundation of China (31901240 and 31971792), the Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2016-AII), and the Central Public-interest Scientific Institution Basal Research Funds, China (Y2022QC17 and CAAS-ZDRW202107).
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| About author: Received 31 May, 2022 Accepted 6 September, 2022
GUAN Zhi-bin, E-mail: guanzhibin@caas.cn; Correspondence CHAI Xiu-juan, Tel: +86-10-82106286, E-mail: chaixiujuan@caas.cn; SUN Tan, Tel: +86-10-82105162, E-mail: suntan@caas.cn |
Cite this article:
GUAN Zhi-bin, ZHANG Yan-qi, CHAI Xiu-juan, CHAI Xin, ZHANG Ning, ZHANG Jian-hua, SUN Tan.
2023.
Visual learning graph convolution for multi-grained orange quality grading. Journal of Integrative Agriculture, 22(1): 279-291.
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