Exploring the Path of Digital Transformation to Help Tobacco Industry Realize Intelligent Manufacturing

In order to further improve the ability of tobacco quality control and realize the classification management of tobacco products, the research centers around the determination of tobacco quality, and carries out quantitative and qualitative evaluation of tobacco quality based on near-infrared spectroscopy technology and deep learning. The outcomes revealed that the feature processing method of the quantitative analysis model designed by the study takes less value than other feature extraction methods in three error indicators, the average absolute error of nicotine is only 0.205, and the average absolute percentage error of total sugar is only 0.241; and the value of R-squared indicator reaches 0.920. The accuracy of the tobacco grade determination model is up to 98.62%, and the determination time is only 13.61s, which is higher than that of other models. The accuracy of the tobacco grade determination model can reach 98.62%, and the determination time is only 13.61 s, which is 36.65 s lower than other models. The sensitivity and specificity of the model for different grades of tobacco are better than other baseline models, with the highest sensitivity of 97.66% and the highest specificity of 99.28%. The design of the study can effectively accelerate the efficiency and precision of tobacco quality grading determination and ensure the production quality of tobacco products.