Research on a random forest-based tobacco quality classification model

As intelligent technologies rapidly advance, the tobacco industry faces both opportunities and challenges in enhancing production efficiency and product quality. Quality control is a crucial aspect of tobacco shredding, but traditional manual inspection methods are no longer adequate to meet the demands for efficiency and precision. To tackle this issue, this study proposes a tobacco quality prediction model based on the Random Forest Algorithm. The aim is to enhance the accuracy and reliability of quality assessment by analyzing key process parameters in the shredding process. The model was trained and evaluated using cross-validation, and experimental results demonstrate its high prediction accuracy (over 93%) and robust generalization capability. Additionally, feature importance analysis has revealed the contribution of each key process parameter to tobacco quality, offering scientific insights for optimizing production processes. This study provides new theoretical foundations and practical guidance for intelligent production and quality control in the tobacco industry.