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<p>This paper utilizes the fusion model of YOLOv8 and LSTM to construct an intelligent monitoring and early warning system for the elasticity of the traditional Chinese medicine industry chain. This system combines all the efficient target detection capabilities of the YOLOv8 algorithm with the advantages of the LSTM neural network in processing time series data, and proposes a brand-new intelligent monitoring method. This system can conduct real-time monitoring of key links in the traditional Chinese medicine industry chain. It can also provide accurate risk warnings and predictions by analyzing time series data. This can enhance the stability and flexibility of the traditional Chinese medicine industry chain. In the process of data analysis, the experiment utilized multimodal datasets from various links of the traditional Chinese medicine industry chain. The visual data is processed by the YOLOv8 model, while the digital time series data is processed by the LSTM network. Moreover, this system was compared with traditional models such as YOLOv4 and YOLOv5. The experimental results showed that the accuracy rate of the YOLOv8 model reached 90%, the recall rate was 88%, and the F1 score was 89%. In terms of accuracy rate, recall rate, and F1 score, it is significantly better than other versions. When YOLOv8 and LSTM are integrated, the performance of the YOLOv8-LSTM model becomes even more prominent. Its accuracy rate can reach 92%, the recall rate can reach 91%, and the F1 score can also reach 91%. These data demonstrate that The YOLOv8-LSTM fusion model has significant advantages in dynamically monitoring risk factors in the traditional Chinese medicine industry chain. The YOLOv8-LSTM model shows high accuracy when predicting the resilience of the traditional Chinese medicine industry chain, especially in long-term prediction, the model can maintain a prediction accuracy of up to 75%, while the accuracy of YOLOv8 and YOLOv4-LSTM is significantly reduced. Through these experimental data, this paper proves that the integration of YOLOv8 and LSTM can greatly improve the intelligent monitoring and early warning capabilities of the traditional Chinese medicine industry chain, and provide strong decision support for governments and enterprises.</p>

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YOLOv8 integrates LSTM intelligent monitoring and early warning research on the resilience of the traditional Chinese medicine industry chain

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月16日

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  • Meng Lv,
  • Ke Chen,
  • Lina He

摘要

This paper utilizes the fusion model of YOLOv8 and LSTM to construct an intelligent monitoring and early warning system for the elasticity of the traditional Chinese medicine industry chain. This system combines all the efficient target detection capabilities of the YOLOv8 algorithm with the advantages of the LSTM neural network in processing time series data, and proposes a brand-new intelligent monitoring method. This system can conduct real-time monitoring of key links in the traditional Chinese medicine industry chain. It can also provide accurate risk warnings and predictions by analyzing time series data. This can enhance the stability and flexibility of the traditional Chinese medicine industry chain. In the process of data analysis, the experiment utilized multimodal datasets from various links of the traditional Chinese medicine industry chain. The visual data is processed by the YOLOv8 model, while the digital time series data is processed by the LSTM network. Moreover, this system was compared with traditional models such as YOLOv4 and YOLOv5. The experimental results showed that the accuracy rate of the YOLOv8 model reached 90%, the recall rate was 88%, and the F1 score was 89%. In terms of accuracy rate, recall rate, and F1 score, it is significantly better than other versions. When YOLOv8 and LSTM are integrated, the performance of the YOLOv8-LSTM model becomes even more prominent. Its accuracy rate can reach 92%, the recall rate can reach 91%, and the F1 score can also reach 91%. These data demonstrate that The YOLOv8-LSTM fusion model has significant advantages in dynamically monitoring risk factors in the traditional Chinese medicine industry chain. The YOLOv8-LSTM model shows high accuracy when predicting the resilience of the traditional Chinese medicine industry chain, especially in long-term prediction, the model can maintain a prediction accuracy of up to 75%, while the accuracy of YOLOv8 and YOLOv4-LSTM is significantly reduced. Through these experimental data, this paper proves that the integration of YOLOv8 and LSTM can greatly improve the intelligent monitoring and early warning capabilities of the traditional Chinese medicine industry chain, and provide strong decision support for governments and enterprises.