Convolutional neural network based on transfer learning for discriminating the fermentation degree of black tea

文献类型： 外文期刊

第一作者： Zhu, Xuesong

作者： Zhu, Xuesong;Ding, Zezhong;Wang, Mei;Dong, Chunwang;Zhu, Xuesong;Liu, Shanjian;Chen, Yulong;Dong, Chunwang;Ding, Zezhong;Dong, Chunwang;Wang, Mei

作者机构：

期刊名称：NPJ SCIENCE OF FOOD （ 影响因子：7.8； 五年影响因子：7.0 ）

ISSN：

年卷期： 2025 年 9 卷 1 期

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收录情况： SCI

摘要： Black tea is among the most widely consumed tea. The fermentation process is crucial for developing the flavor of black tea. Currently, many producers rely on personal experience to gauge fermentation, which can be inconsistent and subjective. Additionally, large models are impractical for use in production. Based on this, this paper introduces a lightweight convolutional neural network utilizing transfer learning to assess the fermentation level of black tea. Initially, we applied a model-based transfer learning strategy and conducted pre-training weight experiments to compare and select the student model and the teacher model. Next, we modified the loss function with PolyLoss and optimizer with AdamW for the student model. Finally, we performed a knowledge distillation experiment on the student model. Results indicated that the improved model's accuracy, precision, recall, and F1 improved by 0.0415, 0.0215, 0.0902, and 0.0645, respectively. This research offers technical assistance for digital production of black tea.

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