Abstract: 【Aim】 To reduce the workload of grassroots plant protection personnel and realize the accurate and real-time monitoring of aphids, a new aphid recognition method has been established. 【Methods】 A new aphid recognition and counting method, which is particularly suitable for edge computing environments, was developed based on the optimized Transformer model and sparse attention mechanism. A data acquisition system was constructed using a camera HDV-56003 and a mobile phone lens for four common aphid species, Myzus persicae, Aphis gossypii, Acythosiphon pisum and Rhopalosiphum padi. WeChat mini program was used to display aphid detection and counting results, and ablation experiments were done to verify the feasibility of the system. 【Results】 The present method achieved excellent levels in key performance indicators such as accuracy, mean average precision (mAP), and frames per second (FPS). Specifically, the accuracy, mAP and FPS of the present method reached 98%, 95% and 52.3, respectively, all of them were superior to those of the traditional methods and other baseline models. In addition, in this experiment a corresponding mobile application program was also developed, allowing agricultural practitioners to directly complete the real-time recognition and counting of aphids in the field environment with the help of smartphones, and greatly improving the convenience and efficiency of operation. 【Conclusion】 The high-precision aphid recognition and counting method based on Transformer in mobile computing scenarios has achieved real-time aphid recognition and counting. This study not only provides new technological solutions for precise recognition of aphids, but also provides important references for the intelligent recognition and monitoring of other agricultural pests, which helps to promote the development of precision agriculture and smart agriculture.

Key words: Aphid, aphid recognition, aphid counting, agricultural pest monitoring, deep learning, mobile computing