Design and implementation of an agricultural insect monitoring system based on edge-cloud collaboration

Le résumé fourni par la source

With the rapid development of IoT and AI technologies, agricultural pest monitoring, as a key link in smart agriculture, plays a crucial role in ensuring food security. This paper designs and implements an agricultural pest monitoring system based on the K230 edge computing platform. The system design combines the YOLO26n detection model with a heterogeneous edge computing architecture, achieving real-time pest detection and environmental data acquisition through hardware and software co-design. Test results demonstrate that the system possesses characteristics of high accuracy, low latency, and high reliability, offering a high-performance edge computing solution for precision plant protection in agriculture. Through a detailed description of the system's hardware architecture and software algorithms, as well as the analysis of test data in real-world scenarios, the effectiveness and practicality of the system in unstructured farmland environments are verified, providing valuable reference for research and applications in related fields.

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