Aphid-ResNetSwin: An Image Recognition Method with Improved Attention Mechanism for Graded Identification of Myzus persicae

<i>Myzus persicae</i> is the most devastating piercing-sucking pest threatening tobacco production. Precise quantification of infestation severity is a prerequisite for precision pest management, making the integration of visual image analysis highly essential for efficient management. Current computer vision models in modern agriculture are primarily designed for classifying various pest species, and there is a lack of image-driven analytical tools for assessing the severity of damage inflicted by a single target pest. To supplement existing analytical tools and enable the graded recognition of tobacco aphid (<i>M. persicae</i>) infestation levels, we propose the Aphid-ResNetSwin model. This model is constructed by integrating a Global Channel-Spatial Attention module (a specialized attention mechanism) into the well-established InceptionResNetV2 architecture. Performance evaluation results demonstrated that the Aphid-ResNetSwin model achieved a graded recognition accuracy of 89.11%. Compared with widely adopted mainstream baseline models in computer vision, such as MobileNetV3, SwinTransformer, and InceptionResNetV2, our proposed model exhibited superior performance in recognition accuracy. Furthermore, the classification accuracy of our model for <i>M. persicae</i> infestation across all severity levels was significantly higher than that of manual identification, with the exception of healthy leaves. Collectively, our findings indicate that the Aphid-ResNetSwin model provides a robust tool for the graded recognition of <i>M. persicae</i> infestation, offering valuable model-based support for the precision control of aphids in tobacco fields.