Research and Application of Automatic Tobacco Baking System Based on Multi-Source Fusion

With the rapid growth of tobacco market demand and the gradual aging of traditional roasting technology, automated tobacco roasting system has become an important means to improve production efficiency and guarantee tobacco quality. In this study, an automated tobacco roasting system based on multi-source data fusion is designed to collect environmental parameters such as temperature, humidity and airflow in the roasting process in real time through IoT devices and edge computing, and combine with deep learning and fuzzy control algorithms to achieve accurate regulation of the roasting curve. The system adopts a modular structure, including a data acquisition and monitoring module, an intelligent algorithm processing unit and a feedback control system, to ensure the efficient and stable operation of the roasting process. In the experimental process, through the comparison and verification of the automated process and traditional process, it is found that the automated system shows significant advantages in the proportion of top-grade tobacco, unit cost and error rate control: the proportion of top-grade tobacco is increased to 50.12%, the unit cost is reduced by 29%, and the error rate is reduced to 0.23%. The system not only greatly improves the appearance quality and market competitiveness of tobacco, but also realises the dynamic adaptation to different varieties of tobacco, providing theoretical basis and practical support for the intelligent development of the tobacco industry.