JIA-2022-0700 面向多粒度柑橘品质分级的视觉学习图卷积方法

doi:10.1016/j.jia.2022.09.019

Journal of Integrative Agriculture ›› 2023, Vol. 22 ›› Issue (1): 279-291.DOI: 10.1016/j.jia.2022.09.019

JIA-2022-0700 面向多粒度柑橘品质分级的视觉学习图卷积方法

收稿日期:2021-05-01 接受日期:2022-09-06 出版日期:2023-01-20 发布日期:2022-09-06

Visual learning graph convolution for multi-grained orange quality grading

GUAN Zhi-bin¹, ZHANG Yan-qi¹, CHAI Xiu-juan¹, CHAI Xin¹, ZHANG Ning^1,², ZHANG Jian-hua^1,³, SUN Tan^2,³

¹Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
²Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, P.R.China
³National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, P.R.China

Received:2021-05-01 Accepted:2022-09-06 Online:2023-01-20 Published:2022-09-06
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
Supported by:
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).

摘要/Abstract

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.

Key words: GCN , multi-view , fine-grained , visual feature , appearance , diameter size

. JIA-2022-0700 面向多粒度柑橘品质分级的视觉学习图卷积方法[J]. Journal of Integrative Agriculture, 2023, 22(1): 279-291.

GUAN Zhi-bin, ZHANG Yan-qi, CHAI Xiu-juan, CHAI Xin, ZHANG Ning, ZHANG Jian-hua, SUN Tan. Visual learning graph convolution for multi-grained orange quality grading[J]. Journal of Integrative Agriculture, 2023, 22(1): 279-291.

参考文献

Azarmdel H, Jahanbakhshi A, Mohtasebi S S, Muñoz A R. 2020. Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM). Postharvest Biology and Technology, 166, 111201.
Ding X, Zhang X, Ma N, Han J, Ding G, Sun J. 2021. RepVGG: Making VGG-style convnets great again. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Institute of Electrical and Electronics Engineers (IEEE), Nashville, TN, USA. pp. 13728–13737.
Fan S, Liang X, Huang W, Zhang V J, Pang Q, He X, Li L, Zhang C. 2022. Real-time defects detection for apple sorting using NIR cameras with pruning-based YOLOV4 network. Computers and Electronics in Agriculture, 193, 106715.
Gurubelli Y, Ramanathan M, Ponnusamy P. 2019. Fractional fuzzy 2DLDA approach for pomegranate fruit grade classification. Computers and Electronics in Agriculture, 162, 95–105.
Hamza R, Chtourou M. 2020. Design of fuzzy inference system for apple ripeness estimation using gradient method. IET Image Processing, 14, 561–569.
He K, Zhang X, Ren S, Sun J. 2016. Deep residual learning for image recognition. In: 2016 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). Institute of Electrical and Electronics Engineers (IEEE), Las Vegas, NV, USA. pp. 770–778.
Hu J, Shen L, Sun G. 2018. Squeeze-and-excitation networks. In: 2018 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). Institute of Electrical and Electronics Engineers (IEEE), Salt Lake City, UT, USA. pp. 7132–7141.
Kumari N, Dwivedi R K, Bhatt A K, Belwal R. 2022. Automated fruit grading using optimal feature selection and hybrid classification by self-adaptive chicken swarm optimization: Grading of mango. Neural Computing and Applications, 34, 1285–1306.
Lu S, Chen W, Zhang X, Karkee M. 2022. Canopy-attention-YOLOv4-based immature/mature apple fruit detection on dense-foliage tree architectures for early crop load estimation. Computers and Electronics in Agriculture, 193, 106696.
Mon T O, ZarAung N. 2020. Vision based volume estimation method for automatic mango grading system. Biosystems Engineering, 198, 338–349.
Ortega-Sánchez N, Rodríguez-Esparza E, Oliva D, Pérez-Cisneros M, Mohamed A W, Dhiman G, Hernández-Montelongo R. 2022. Identification of apple diseases in digital images by using the Gaining-sharing knowledge-based algorithm for multilevel thresholding. Soft Computing, 26, 2587–2623.
Peng Z, Huang W, Gu S, Xie L, Wang Y, Jiao J, Ye Q. 2021. Conformer: local features coupling global representations for visual recognition. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Institute of Electrical and Electronics Engineers (IEEE), Montreal, QC, Canada. pp. 357–366.
Rodriguez M, Pastor F, Ugarte W. 2021. Classification of fruit ripeness grades using a convolutional neural network and data augmentation. In: 2021 28th Conference of Open Innovations Association (FRUCT). Institute of Electrical and Electronics Engineers (IEEE), Moscow, Russia.
Simonyan K, Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR). Conference Track Proceedings, San Diego, CA, USA.
Tan M, Le Q V. 2019. EfficientNet: Rethinking model scaling for convolutional neural networks. In: Proceedings of the 36th International Conference on Machine Learning (ICML). Long Beach, California, USA. pp. 6105–6114.
Tang Y, Gao S, Zhuang J, Hou C, He Y, Chu X, Miao A, Luo S. 2020. Apple bruise grading using piecewise nonlinear curve fitting for hyperspectral imaging data. IEEE Access, 8, 147494–147506.
Tian S, Wang S, Xu H. 2022. Early detection of freezing damage in oranges by online Vis/NIR transmission coupled with diameter correction method and deep 1D-CNN. Computers and Electronics in Agriculture, 193, 106638.
Tripathi M K, Maktedar D D. 2021. Optimized deep learning model for mango grading: Hybridizing lion plus firefly algorithm. IET Image Processing, 15, 1940–1956.
Vu T, Kang H, Yoo C D. 2021. SCNet: Training inference sample consistency for instance segmentation. In: Proceedings of the 35th AAAI (Association for the Advancement of Artificial Intelligence) Conference on Artificial Intelligence. AAAI Press 2021. pp. 2701–2709.
Wang J, Sun K, Cheng T, Jiang B, Deng C, Zhao Y, Liu D, Mu Y, Tan M, Wang X, Liu W, Xiao B. 2021. Deep high-resolution representation learning for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 3349–3364.
Xie S, Girshick R, Dollár P, Tu Z, He K. 2017. Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Institute of Electrical and Electronics Engineers (IEEE), Honolulu, HI, USA. pp. 5987–5995.
Yuan L, Chen Y, Wang T, Yu W, Shi Y, Jiang Z, Tay F E H, Feng J, Yan S. 2021. Tokens-to-token ViT: training vision transformers from scratch on imageNet. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Institute of Electrical and Electronics Engineers (IEEE), Montreal, QC, Canada. pp. 538–547.
Zhang Y E, Liu Y P. 2021. Identification of navel orange diseases and pests based on the fusion of densenet and self-attention mechanism. Computational Intelligence and Neuroscience, 2021, doi: 10.1155/2021/5436729.

JIA-2022-0700 面向多粒度柑橘品质分级的视觉学习图卷积方法

Visual learning graph convolution for multi-grained orange quality grading

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