{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T14:40:18Z","timestamp":1785249618278,"version":"3.55.0"},"reference-count":29,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T00:00:00Z","timestamp":1709078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Special Project on Regional Collaborative Innovation in Xinjiang Uygur Autonomous Region","award":["2022E02035"],"award-info":[{"award-number":["2022E02035"]}]},{"name":"Special Project on Regional Collaborative Innovation in Xinjiang Uygur Autonomous Region","award":["ZY2023M064"],"award-info":[{"award-number":["ZY2023M064"]}]},{"name":"Special Project on Regional Collaborative Innovation in Xinjiang Uygur Autonomous Region","award":["2023010201020465"],"award-info":[{"award-number":["2023010201020465"]}]},{"name":"Special Project on Regional Collaborative Innovation in Xinjiang Uygur Autonomous Region","award":["202310524017"],"award-info":[{"award-number":["202310524017"]}]},{"name":"Hubei Provincial Administration of Traditional Chinese Medicine Research Project on Traditional Chinese Medicine","award":["2022E02035"],"award-info":[{"award-number":["2022E02035"]}]},{"name":"Hubei Provincial Administration of Traditional Chinese Medicine Research Project on Traditional Chinese Medicine","award":["ZY2023M064"],"award-info":[{"award-number":["ZY2023M064"]}]},{"name":"Hubei Provincial Administration of Traditional Chinese Medicine Research Project on Traditional Chinese Medicine","award":["2023010201020465"],"award-info":[{"award-number":["2023010201020465"]}]},{"name":"Hubei Provincial Administration of Traditional Chinese Medicine Research Project on Traditional Chinese Medicine","award":["202310524017"],"award-info":[{"award-number":["202310524017"]}]},{"name":"Wuhan knowledge innovation special Dawn project","award":["2022E02035"],"award-info":[{"award-number":["2022E02035"]}]},{"name":"Wuhan knowledge innovation special Dawn project","award":["ZY2023M064"],"award-info":[{"award-number":["ZY2023M064"]}]},{"name":"Wuhan knowledge innovation special Dawn project","award":["2023010201020465"],"award-info":[{"award-number":["2023010201020465"]}]},{"name":"Wuhan knowledge innovation special Dawn project","award":["202310524017"],"award-info":[{"award-number":["202310524017"]}]},{"name":"National innovation and entrepreneurship training program for college students","award":["2022E02035"],"award-info":[{"award-number":["2022E02035"]}]},{"name":"National innovation and entrepreneurship training program for college students","award":["ZY2023M064"],"award-info":[{"award-number":["ZY2023M064"]}]},{"name":"National innovation and entrepreneurship training program for college students","award":["2023010201020465"],"award-info":[{"award-number":["2023010201020465"]}]},{"name":"National innovation and entrepreneurship training program for college students","award":["202310524017"],"award-info":[{"award-number":["202310524017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The identification and classification of traditional Chinese herbal medicines demand significant time and expertise. We propose the dual-teacher supervised decay (DTSD) approach, an enhancement for Chinese herbal medicine recognition utilizing a refined knowledge distillation model. The DTSD method refines output soft labels, adapts attenuation parameters, and employs a dynamic combination loss in the teacher model. Implemented on the lightweight MobileNet_v3 network, the methodology is deployed successfully in a mobile application. Experimental results reveal that incorporating the exponential warmup learning rate reduction strategy during training optimizes the knowledge distillation model, achieving an average classification accuracy of 98.60% for 10 types of Chinese herbal medicine images. The model boasts an average detection time of 0.0172 s per image, with a compressed size of 10 MB. Comparative experiments demonstrate the superior performance of our refined model over DenseNet121, ResNet50_vd, Xception65, and EfficientNetB1. This refined model not only introduces an approach to Chinese herbal medicine image recognition but also provides a practical solution for lightweight models in mobile applications.<\/jats:p>","DOI":"10.3390\/s24051559","type":"journal-article","created":{"date-parts":[[2024,2,28]],"date-time":"2024-02-28T10:37:36Z","timestamp":1709116656000},"page":"1559","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Enhanced Knowledge Distillation for Advanced Recognition of Chinese Herbal Medicine"],"prefix":"10.3390","volume":"24","author":[{"given":"Lu","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Computer Science, South-Central Minzu University, Wuhan 430074, China"},{"name":"Key Laboratory of Information Physics Integration and Intelligent Computing of National Ethnic Affairs Commission, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhan","family":"Long","sequence":"additional","affiliation":[{"name":"College of Computer Science, South-Central Minzu University, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junchao","family":"Yi","sequence":"additional","affiliation":[{"name":"College of Computer Science, South-Central Minzu University, Wuhan 430074, China"},{"name":"Hubei Provincial Engineering Research Center of Agricultural Blockchain and Intelligent Management, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer Science, South-Central Minzu University, Wuhan 430074, China"},{"name":"Hubei Provincial Engineering Research Center of Agricultural Blockchain and Intelligent Management, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"ref_1","first-page":"44","article-title":"Research and implementation of Chinese herbal medicine plant image classification based on alexnet deep learning model","volume":"34","author":"Huang","year":"2020","journal-title":"J. Qilu Univ. Technol."},{"key":"ref_2","first-page":"1931","article-title":"Natural grassland plant species identification method based on deep learning","volume":"37","author":"Gao","year":"2020","journal-title":"Grassl. Sci."},{"key":"ref_3","first-page":"1","article-title":"Classification and recognition of Chinese herbal medicine based on deep learning","volume":"6","author":"Zhang","year":"2020","journal-title":"Smart Health"},{"key":"ref_4","first-page":"21","article-title":"Research on Chinese herbal medicine plant image recognition method based on deep learning","volume":"37","author":"Wang","year":"2020","journal-title":"Inf. Tradit. Chin. Med."},{"key":"ref_5","unstructured":"Hu, K. (2020). Research and Implementation of Fritillaria Classification Algorithm Based on Deep Learning. [Master\u2019s Thesis, Chengdu University]."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Shlens, J., and Le, Q.V. (2020, January 14\u201319). Randaugment: Practical automated data augmentation with a reduced search space. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref_7","unstructured":"He, K., Zhang, X., and Ren, S. (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_8","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"He, T., Zhang, Z., and Zhang, H. (2019, January 16\u201320). Bag of Tricks for Image Classification with Convolutional Neural Networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00065"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"8704","DOI":"10.1109\/TPAMI.2019.2918284","article-title":"Convolutional Networks with Dense Connectivity","volume":"44","author":"Huang","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","first-page":"162","article-title":"Tomato leaf diseases recognition based on improved multi-scale AlexNet","volume":"35","author":"Guo","year":"2019","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"107608","DOI":"10.1016\/j.patcog.2020.107608","article-title":"Topological optimization of the DenseNet with pretrained-weights inheritance and genetic channel selection","volume":"109","author":"Fang","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/978-1-4842-6168-2_11","article-title":"MobileNetV3","volume":"Volume 1","author":"Koonce","year":"2021","journal-title":"Convolutional Neural Networks with Swift for Tensorflow"},{"key":"ref_14","unstructured":"Rosebrock, A. (2017). Deep Learning for Computer Vision with Python-Starter Bundle, PyImageSearch."},{"key":"ref_15","first-page":"225","article-title":"Detection of maize leaf diseases using improved MobileNet V3-small","volume":"16","author":"Gao","year":"2023","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100607","DOI":"10.1016\/j.imu.2021.100607","article-title":"An easy method for identifying 315 categories of commonly-used Chinese herbal medicines based on automated image recognition using AutoML platforms","volume":"25","author":"Chen","year":"2021","journal-title":"Inform. Med. Unlocked"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1111\/ppa.13322","article-title":"Attention embedded lightweight network for maize disease recognition","volume":"70","author":"Chen","year":"2021","journal-title":"Plant Pathol."},{"key":"ref_18","first-page":"181","article-title":"Detection method of citrus based on deep convolution neural network","volume":"50","author":"Bi","year":"2019","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_19","unstructured":"Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the knowledge in a neural network. arXiv."},{"key":"ref_20","first-page":"26","article-title":"Survey on transfer learning research","volume":"26","author":"Zhuang","year":"2015","journal-title":"J. Softw."},{"key":"ref_21","first-page":"2508","article-title":"Survey of convolutional neural network","volume":"36","author":"Li","year":"2016","journal-title":"J. Comput. Appl."},{"key":"ref_22","first-page":"186","article-title":"Disease recognition system for greenhouse cucumbers based on deep convolutional neural network","volume":"34","author":"Ma","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng. (Trans. CSAE)"},{"key":"ref_23","first-page":"816","article-title":"Deep learning-based decision support system for weeds detection in wheat fields","volume":"12","author":"Jabir","year":"2022","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_24","first-page":"1","article-title":"A guide to convolutional neural networks for computer vision","volume":"8","author":"Khan","year":"2018","journal-title":"Synth. Lect. Comput. Vis."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"105672","DOI":"10.1016\/j.compag.2020.105672","article-title":"A review of computer vision technologies for plant phenotyping","volume":"176","author":"Li","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patcog.2017.06.036","article-title":"Multi-modal feature fusion for geographic image annotation","volume":"73","author":"Li","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_27","first-page":"114","article-title":"Plant trait estimation and classification studies in plant phenotyping using machine vision\u2014A review","volume":"10","author":"Kolhar","year":"2023","journal-title":"Inf. Process. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3880","DOI":"10.1109\/JSTARS.2018.2866595","article-title":"Deep multiple feature fusion for hyperspectral image classification","volume":"11","author":"Cao","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1559\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:06:34Z","timestamp":1760105194000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/5\/1559"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,28]]},"references-count":29,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["s24051559"],"URL":"https:\/\/doi.org\/10.3390\/s24051559","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,28]]}}}