Evolutionary algorithm-optimized feature fusion for accurate classification of shredded tobacco using multi-sensor data

Our framework effectively balances information from the three sensing modalities to maximize their complementary strengths. This work confirms that evolutionary algorithm-based feature fusion is a powerful and robust method for unlocking the full potential of multi-sensor data, thereby significantly advancing the accuracy of complex plant material classification.