| Summary: | The research of the bulk tobacco flue-curing process is helpful to model the intelligent bulk curing system, which is designed to implement the curing procedure without manual operation. An intelligent bulk curing method based on the convolutional neural network (CNN) named TobaccoNet was proposed, which could set the target dry-bulb temperature (<inline-formula> <tex-math notation="LaTeX">$T_{\mathrm {D}}$ </tex-math></inline-formula>) and the target wet-bulb temperature (<inline-formula> <tex-math notation="LaTeX">$T_{\mathrm {W}}$ </tex-math></inline-formula>) of the bulk curing barn according to the tobacco leaves image. The performance of the TobaccoNet is compared with the traditional manual image feature extraction method, the stacked-sparse-autoencoder (SSAE)-based deep learning method, and the other two methods applied in related references. The test results show that TobaccoNet outperforms the comparison methods in predicting <inline-formula> <tex-math notation="LaTeX">$T_{\mathrm {D}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$T_{\mathrm {W}}$ </tex-math></inline-formula>. Specifically, the correlation coefficient reaches 0.9965 and 0.9683, the mean relative error is 1.62% and 1.77%, and the root mean squared error achieves 1.061 °C and 0.8581 °C respectively. The promising results demonstrate that TobaccoNet is effective and reliable for modeling the intelligent bulk tobacco flue-curing process. The influence of different CNN structures on the prediction accuracy of <inline-formula> <tex-math notation="LaTeX">$T_{\mathrm {D}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$T_{\mathrm {W}}$ </tex-math></inline-formula> was analyzed. From the perspective of the computational complexity and the prediction performance, the proposed sequential CNN structure is more suitable for analyzing bulk tobacco curing in this study.
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