{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T19:58:50Z","timestamp":1766087930327,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T00:00:00Z","timestamp":1666396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China projects","doi-asserted-by":"publisher","award":["81873010","82173979","CI2021A04204"],"award-info":[{"award-number":["81873010","82173979","CI2021A04204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"scientific and technological innovation project of the China Academy of Chinese Medical Sciences","award":["81873010","82173979","CI2021A04204"],"award-info":[{"award-number":["81873010","82173979","CI2021A04204"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gardeniae Fructus (GF) is one of the most widely used traditional Chinese medicines (TCMs). Its processed product, Gardeniae Fructus Praeparatus (GFP), is often used as medicine; hence, there is an urgent need to determine the stir-frying degree of GFP. In this paper, we propose a deep learning method based on transfer learning to determine the stir-frying degree of GFP. We collected images of GFP samples with different stir-frying degrees and constructed a dataset containing 9224 images. Five neural networks were trained, including VGG16, GoogLeNet, Resnet34, MobileNetV2, and MobileNetV3. While the model weights from ImageNet were used as initial parameters of the network, fine-tuning was used for four neural networks other than MobileNetV3. In the training of MobileNetV3, both feature transfer and fine-tuning were adopted. The accuracy of all five models reached more than 95.82% in the test dataset, among which MobileNetV3 performed the best with an accuracy of 98.77%. In addition, the results also showed that fine-tuning was better than feature transfer in the training of MobileNetV3. Therefore, we conclude that deep learning can effectively recognize the stir-frying degree of GFP.<\/jats:p>","DOI":"10.3390\/s22218091","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T10:09:23Z","timestamp":1666606163000},"page":"8091","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Determining the Stir-Frying Degree of Gardeniae Fructus Praeparatus Based on Deep Learning and Transfer Learning"],"prefix":"10.3390","volume":"22","author":[{"given":"Yuzhen","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Chongyang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Yun","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5412-5202","authenticated-orcid":false,"given":"Pengle","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Technology, Beijing Forestry University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"114984","DOI":"10.1016\/j.jep.2022.114984","article-title":"A Review of the Ethnopharmacology, Phytochemistry, Pharmacology and Toxicology of Fructus Gardeniae (Zhi-Zi)","volume":"289","author":"Tian","year":"2022","journal-title":"J. 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