A HYBRID TCN-LSTM MODEL FOR ACCURATE TOBACCO CURING STATE RECOGNITION

The curing of tobacco is a critical process that determines the quality of the final product. Accurate recognition of tobacco curing states is essential for ensuring optimal quality. Existing recognition models mostly focus on the transient states within the curing barn. In contrast, this study incorporates multiple time steps to capture dynamic feature changes in the curing barn over time, providing a more accurate state recognition. A hybrid deep learning model combining Temporal Convolutional Networks (TCN), Long Short-Term Memory (LSTM) networks, and a novel Density-aware Channel Redistribution Unit (DCRU) based on Kernel Density Estimation is proposed. The model integrates the global feature extraction capability of TCN, the long-term dependency modeling strength of LSTM, and the complex channel feature extraction ability of DCRU, thereby enhancing the model's performance in recognizing the stages of tobacco leaf curing. Tests conducted on a real-world tobacco dataset demonstrate that the model achieves a prediction accuracy of 0.989 and outperforms baseline models as well as existing tobacco curing state recognition methods. These results validate the effectiveness of the hybrid TCN-LSTM model in recognizing tobacco leaf curing states, with promising applications in agricultural automation.