{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T06:06:22Z","timestamp":1775887582627,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T00:00:00Z","timestamp":1681344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Regional Specialized Industry Development Plus Program","award":["S3268440"],"award-info":[{"award-number":["S3268440"]}]},{"name":"Ministry of SMEs and Startups (MSS, Republic of Korea)","award":["S3268440"],"award-info":[{"award-number":["S3268440"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we addressed the challenges in sorting high-yield apple cultivars that traditionally relied on manual labor or system-based defect detection. Existing single-camera methods failed to uniformly capture the entire surface of apples, potentially leading to misclassification due to defects in unscanned areas. Various methods were proposed where apples were rotated using rollers on a conveyor. However, since the rotation was highly random, it was difficult to scan the apples uniformly for accurate classification. To overcome these limitations, we proposed a multi-camera-based apple sorting system with a rotation mechanism that ensured uniform and accurate surface imaging. The proposed system applied a rotation mechanism to individual apples while simultaneously utilizing three cameras to capture the entire surface of the apples. This method offered the advantage of quickly and uniformly acquiring the entire surface compared to single-camera and random rotation conveyor setups. The images captured by the system were analyzed using a CNN classifier deployed on embedded hardware. To maintain excellent CNN classifier performance while reducing its size and inference time, we employed knowledge distillation techniques. The CNN classifier demonstrated an inference speed of 0.069 s and an accuracy of 93.83% based on 300 apple samples. The integrated system, which included the proposed rotation mechanism and multi-camera setup, took a total of 2.84 s to sort one apple. Our proposed system provided an efficient and precise solution for detecting defects on the entire surface of apples, improving the sorting process with high reliability.<\/jats:p>","DOI":"10.3390\/s23083968","type":"journal-article","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T02:03:38Z","timestamp":1681437818000},"page":"3968","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Multi-Camera-Based Sorting System for Surface Defects of Apples"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1553-9637","authenticated-orcid":false,"given":"Ju-Hwan","family":"Lee","sequence":"first","affiliation":[{"name":"Department of ICT Convergence System Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6969-3042","authenticated-orcid":false,"given":"Hoang-Trong","family":"Vo","sequence":"additional","affiliation":[{"name":"Department of ICT Convergence System Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gyeong-Ju","family":"Kwon","sequence":"additional","affiliation":[{"name":"LINUXIT, 53-18, Geumbong-ro 44beon-gil, Gwangsan-gu, Gwangju 62377, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyoung-Gook","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electronic Convergence Engineering, Kwangwoon University, 20 Gwangun-ro, Nowon-gu, Seoul 01897, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin-Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of ICT Convergence System Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compag.2015.05.021","article-title":"Sensors and systems for fruit detection and localization: A review","volume":"116","author":"Gongal","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.biosystemseng.2019.11.011","article-title":"Machine learning applications to non-destructive defect detection in horticultural products","volume":"189","author":"Nturambirwe","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Feng, J., Zeng, L., and He, L. (2019). Apple fruit recognition algorithm based on multi-spectral dynamic image analysis. Sensors, 19.","DOI":"10.3390\/s19040949"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"052058","DOI":"10.1088\/1757-899X\/862\/5\/052058","article-title":"Vision system for detection of defects on apples using hyperspectral imaging coupled with neural network and Haar cascade algorithm","volume":"862","author":"Balabanov","year":"2020","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_5","first-page":"24","article-title":"Real-time visual inspection system for grading fruits using computer vision and deep learning techniques","volume":"9","author":"Ismail","year":"2022","journal-title":"Inf. Process. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"292681","DOI":"10.1155\/2014\/292681","article-title":"A real-time apple grading system using multicolor space","volume":"2014","author":"Toylan","year":"2014","journal-title":"Sci. World J."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yue, X., and Tian, N. (2022, January 18\u201320). Research on Apple Classification System Based on Hybrid Kernel Function and Multi-Feature Fusion. Proceedings of the 2020 International Conference on Computer Engineering and Application (ICCEA), Guangzhou, China.","DOI":"10.1109\/ICCEA50009.2020.00129"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111588","DOI":"10.1016\/j.postharvbio.2021.111588","article-title":"Development and evaluation of an apple infield grading and sorting system","volume":"180","author":"Zhang","year":"2021","journal-title":"Postharvest Biol. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"473","DOI":"10.3390\/agriengineering5010031","article-title":"Development and Evaluation of a Small-Scale Apple Sorting Machine Equipped with a Smart Vision System","volume":"5","author":"Baneh","year":"2023","journal-title":"AgriEngineering"},{"key":"ref_10","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., and Houlsby, N. (2020, January 23\u201328). Big transfer (bit): General visual representation learning. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Part V 16.","DOI":"10.1007\/978-3-030-58558-7_29"},{"key":"ref_13","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhai, X., Kolesnikov, A., Houlsby, N., and Beyer, L. (2022, January 19\u201320). Scaling vision transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01179"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1109\/JPROC.2020.2976475","article-title":"Model compression and hardware acceleration for neural networks: A comprehensive survey","volume":"108","author":"Deng","year":"2020","journal-title":"Proc. IEEE"},{"key":"ref_16","unstructured":"Han, S., Pool, J., Tran, J., and Dally, W. (2015, January 7\u201312). Learning both weights and connections for efficient neural network. Proceedings of the NIPS 28: Annual Conference on Neural Information Processing Systems 2015, Montreal, QC, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., and Kalenichenko, D. (2018, January 18\u201323). Quantization and training of neural networks for efficient integer-arithmetic-only inference. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00286"},{"key":"ref_18","unstructured":"Han, S., Mao, H., and Dally, W.J. (2015). Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","article-title":"Knowledge distillation: A survey","volume":"129","author":"Gou","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.compag.2009.09.014","article-title":"In-line detection of apple defects using three color cameras system","volume":"70","author":"Zou","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.compag.2016.06.030","article-title":"Design of an automatic apple sorting system using machine vision","volume":"127","author":"Sofu","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"110102","DOI":"10.1016\/j.jfoodeng.2020.110102","article-title":"On line detection of defective apples using computer vision system combined with deep learning methods","volume":"286","author":"Fan","year":"2020","journal-title":"J. Food Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jijesh, J.J., Revathi, D.C., Shivaranjini, M., and Sirisha, R. (2020, January 12\u201313). Development of Machine Learning based Fruit Detection and Grading system. Proceedings of the 2020 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Bangalore, India.","DOI":"10.1109\/RTEICT49044.2020.9315601"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hu, G., Zhang, E., Zhou, J., Zhao, J., Gao, Z., Sugirbay, A., Jin, H., Zhang, S., and Chen, J. (2021). Infield Apple Detection and Grading Based on Multi-Feature Fusion. Horticulturae, 7.","DOI":"10.3390\/horticulturae7090276"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106715","DOI":"10.1016\/j.compag.2022.106715","article-title":"Real-time defects detection for apple sorting using NIR cameras with pruning-based YOLOV4 network","volume":"193","author":"Fan","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kuo, H.H., Barik, D.S., Zhou, J.Y., Hong, Y.K., Yan, J.J., and Yen, M.H. (2022, January 7\u20139). Design and Implementation of AI aided Fruit Grading Using Image Recognition. Proceedings of the 2022 IEEE\/ACIS 23rd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel\/Distributed Computing (SNPD), Taichung, Taiwan.","DOI":"10.1109\/SNPD54884.2022.10051810"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liang, X., Jia, X., Huang, W., He, X., Li, L., Fan, S., Li, J., Zhao, C., and Zhang, C. (2022). Real-Time Grading of Defect Apples Using Semantic Segmentation Combination with a Pruned YOLO V4 Network. Foods, 11.","DOI":"10.3390\/foods11193150"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Endo, M., and Kawamoto, P.N. (2022, January 19\u201321). Tuning Small Datasets for a Custom Apple Sorting System based on Deep Learning. Proceedings of the 2022 Fourth International Conference on Transdisciplinary AI (TransAI), Laguna Hills, CA, USA.","DOI":"10.1109\/TransAI54797.2022.00024"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xu, B., Cui, X., Ji, W., Yuan, H., and Wang, J. (2023). Apple Grading Method Design and Implementation for Automatic Grader Based on Improved YOLOv5. Agriculture, 13.","DOI":"10.3390\/agriculture13010124"},{"key":"ref_30","unstructured":"(2023, March 16). Available online: https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/embedded-systems\/jetson-nano\/."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106230","DOI":"10.1016\/j.compag.2021.106230","article-title":"Real-time recognition system of soybean seed full-surface defects based on deep learning","volume":"187","author":"Zhao","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","unstructured":"Xiao, K., Engstrom, L., Ilyas, A., and Madry, A. (2020). Noise or signal: The role of image backgrounds in object recognition. arXiv."},{"key":"ref_33","unstructured":"Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the knowledge in a neural network. arXiv."},{"key":"ref_34","unstructured":"Tan, M., and Le, Q. (2019, January 10\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., and Doll\u00e1r, P. (2020, January 14\u201319). Designing network design spaces. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"ref_36","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Beyer, L., Zhai, X., Royer, A., Markeeva, L., Anil, R., and Kolesnikov, A. (2022, January 19\u201320). Knowledge distillation: A good teacher is patient and consistent. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01065"},{"key":"ref_38","unstructured":"(2023, March 16). Available online: https:\/\/github.com\/dusty-nv\/jetson-inference."},{"key":"ref_39","unstructured":"(2023, March 16). Available online: https:\/\/onnx.ai."},{"key":"ref_40","unstructured":"You, Y., Li, J., Reddi, S., Hseu, J., Kumar, S., Bhojanapalli, S., Song, X., Demmel, J., Keutzer, K., and Hsieh, C.J. (2019). Large batch optimization for deep learning: Training bert in 76 minutes. arXiv."},{"key":"ref_41","unstructured":"Menon, A.K., Rawat, A.S., Reddi, S.J., Kim, S., and Kumar, S. (2020). Why distillation helps: A statistical perspective. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/8\/3968\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:15:52Z","timestamp":1760123752000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/8\/3968"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,13]]},"references-count":41,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["s23083968"],"URL":"https:\/\/doi.org\/10.3390\/s23083968","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,13]]}}}