A Transformer-Based Detection Network for Precision Cistanche Pest and Disease Management in Smart Agriculture.

This study focuses on pest and disease detection in cistanche, proposing a Transformer-based object detection network enhanced by a bridging attention mechanism and bridging loss function, demonstrating outstanding performance in complex agricultural scenarios. The bridging attention mechanism dynamically fuses low-level details and high-level semantics, significantly improving detection capabilities for small targets and complex backgrounds. Experimental results show that the method achieves an average accuracy of 0.93, a precision of 0.95, a recall of 0.92, and mAP@50 and mAP@75 scores of 0.92 and 0.90, outperforming traditional self-attention mechanisms and CBAM modules. These results confirm the method's ability to overcome challenges such as unclear disease features and small target sizes, providing robust support for precision pest detection. The research contributes to smart agricultural disease management and the sustainable development of cistanche cultivation while laying a solid foundation for future agricultural intelligence applications.