In the field of smart plant protection, accurate early monitoring of winged aphids is critical, as it enables the interruption of viral disease transmission and reduces dependence on pesticides. In response to the core challenges of low efficiency in manual counting associated with current sticky trap-based monitoring, as well as the insufficient recognition accuracy and poor robustness of computer vision models in dense small-target scenarios, this study aims to develop a high-precision, highly reliable automated identification method for winged aphids. To achieve this, a specialized detection model named PCSNet is proposed. Based on YOLOv12, this model innovatively incorporates a coordinate attention mechanism to enhance the perception of spatial structures for small targets. Simultaneously, a shallow feature enhancement branch (SFEB) is introduced to enrich detailed information, and the Normalized Wasserstein Distance loss function is integrated to optimize bounding box regression. Comparative experiments conducted on a self-constructed dataset of sticky trap images encompassing complex field backgrounds demonstrate that the PCSNet model achieves optimal detection performance, with a mean average precision (mAP) of 0.791 and a precision of 0.866, significantly outperforming mainstream detection models and various attention mechanism variants. This research provides an effective technical solution for constructing a real-time and automated intelligent pest monitoring system, offering substantial application value for advancing the intelligent transformation of pest and disease monitoring and promoting practices in green prevention and control.
Guan et al. (Wed,) studied this question.