Implementation and optimization of intelligent monitoring technology in tobacco leaf outbound process

This project developed an intelligent monitoring system based on deep learning and big data analysis to address grade recognition and anomaly detection in tobacco leaf warehouse logistics. By enhancing the CRNN algorithm and anomaly detection mechanism, the system achieved automatic tobacco leaf grade recognition. The implementation yielded remarkable results: 99.1% image recognition accuracy, 150% increase in processing efficiency, 55% reduction in labor costs, daily processing capacity increased from 800 to 1200 boxes, and annual economic losses reduced by 1.8 million yuan, significantly improving warehouse management intelligence.