| 采用改进稠密连接网络的防风药材的道地性识别 |
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| 引用本文： | 李东明,汤鹏,张丽娟,雷雨,刘双利.采用改进稠密连接网络的防风药材的道地性识别[J].农业工程学报,2022,38(3):276-285. |
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| 作者姓名： | 李东明 汤鹏 张丽娟 雷雨 刘双利 |
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| 作者单位： | 1. 吉林农业大学信息技术学院，长春 130118;;2. 长春工业大学计算机科学与工程学院，长春 130012;;3. 吉林农业大学中药材学院，长春 130118; |
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| 基金项目： | 国家自然科学基金青年科学基金项目（No.61801439）；吉林省科技厅重点研发项目（20210204050YY）；吉林省教育厅科研项目(JJKH20210747KJ);吉林省环保厅项目（No. 202107）；通辽市科技局重点研发项目（TLCXYD202103） |
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| 摘 要： | 目前市场上对防风药材的质量鉴定，仍停留在依靠专业人员的自身经验，对药材表型观察进行划分定级，这样的做法具有一定的主观性和局限性。针对上述问题，该研究建立具有18 543张包含5个主要产区防风药材图像的标准数据集，并基于深度学习的方法改进稠密连接网络来区分防风药材的产地，对防风药材品质进行精确、高效的智能分类，判断防风药材的道地性及品质优劣。该神经网络的具体建立过程为：首先对残差模块进行优化改进，将协调注意力（Coordinate Attention，CA）模型与残差模块进行融合，以增加特征图中待识别区域的特征权值，降低背景信息对识别任务的干扰；然后将改进的残差模块嵌入到稠密连接网络，以减少模型运算参数、增强网络对特征信息的高效利用能力；最后重构全连接层，来适应对新数据集的识别分类，并增强网络的学习能力。在迁移学习和数据扩充方式下新模型的识别准确率可达97.23%；且训练约48轮便可达到收敛状态，极大的提高了收敛速度。该方法能够高效准确地识别防风药材的产地及道地性并有较强的鲁棒性，可为防风药材质量智能鉴定提供参考。
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| 关 键 词： | 图像处理 特征提取 稠密连接网络 注意力机制 防风药材 道地性药材 |
| 收稿时间： | 2021/10/6 0:00:00 |
| 修稿时间： | 2021/12/11 0:00:00 |
Genuine identification for Saposhnikovia divaricata based on improved DenseNet |
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| Li Dongming,Tang Peng,Zhang Lijuan,Lei Yu,Liu Shuangli.Genuine identification for Saposhnikovia divaricata based on improved DenseNet[J].Transactions of the Chinese Society of Agricultural Engineering,2022,38(3):276-285. |
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| Authors: | Li Dongming Tang Peng Zhang Lijuan Lei Yu Liu Shuangli |
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| Institution: | 1. College of Information Technology, Jilin Agricultural University, Changchun 130118, China;;2. College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China;; 3. College of Traditional Chinese Medicine, Jilin Agricultural University, Changchun 130118, China; |
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| Abstract: | Saposhnikovia divaricata has been one of the most widely planted herbs in Northeast Asia nowadays. The current quality identification of medicinal materials in the market depends mainly on the phenotypic observation from the experience of experts, indicating the certain subjectivity and limitations in large-scale production. In this study, an accurate, efficient, and intelligent approach was proposed to identify the genuine medicinal materials of saposhnikovia divaricata using an improved DenseNet. A standard dataset was established with 18,543 images of medicinal materials of saposhnikovia divaricata from five main producing areas using deep learning. A dense connection network was also improved to distinguish the origin, properties, and quality of the medicinal material. A new neural network model was established as follows. Firstly, a residual module was optimized to embed in the coordinate attention (CA)mechanism for the high feature weight of the area to be identified in the feature map. As such, the interference of background information was reduced in the identification task, particularly for the complex images of medicinal materials with only a small difference of phenotype. Then, the improved residual module was integrated with the dense connection network, further reducing the operation parameters of the model for the enhanced utilization rate of feature information. Finally, the fully connected layer was reconstructed to identify the new dataset for better learning of the network. The network model presented much fewer training parameters and faster convergence speed in the training process, further effectively reducing the over-fitting. The coordinate attention mechanism was replaced to compare the network model on the dataset in the improved network. A series of ablation experiments were conducted on the data set of medicinal materials. The experimental results show that the improved network model performed better to identify the origin of medicinal materials, indicating the significant effect of the coordinate attention mechanism on the accuracy of the model. Consequently, the new model can reach the convergence state after about 48 rounds of training, which greatly improves the convergence speed and then evaluate the medicinal properties, with an average accuracy rate of 97.23%.The strong robustness of the improved model can greatly contribute to the quality identification and evaluation standard of medicinal materials. The finding can provide a strong reference to intelligently and automatically identify the quality of medicinal materials. |
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| Keywords: | image processing feature extraction densenet coordinate attention mechanism Saposhnikovia divaricata genuine medicinal materials |
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