Determining the Stir-Frying Degree of Gardeniae Fructus Praeparatus Based on Deep Learning and Transfer Learning

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 21; p. 8091
• Main Authors: Zhang, Yuzhen, Wang, Chongyang, Wang, Yun, Cheng, Pengle
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 22.10.2022; MDPI
• Subjects: Artificial intelligence; Chinese medicine; Datasets; Deep learning; Frying; Gardeniae Fructus Praeparatus; Medical research; Neural networks; process degree; Skin; transfer learning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22218091

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.