Practice and Application of a Crop Pest Identification System

Shuaijie Li; Jiajing Huang; Hui Bai; Min Yang

doi:10.70088/wy84j595

Authors

Shuaijie Li Hainan Vocational University of Science and Technology, Haikou, China Author
Jiajing Huang Hainan Vocational University of Science and Technology, Haikou, China Author
Hui Bai Hainan Vocational University of Science and Technology, Haikou, China Author
Min Yang Hainan Vocational University of Science and Technology, Haikou, China Author

DOI:

https://doi.org/10.70088/wy84j595

Keywords:

crop pest identification, plant disease recognition, intelligent agriculture, internet of things, artificial intelligence, lightweight deep learning, precision plant protection

Abstract

Crop pests and diseases are key bottlenecks restricting the improvement of agricultural production efficiency, stability, and product quality. Traditional recognition modes that rely on manual field inspection and expert experience suffer from high misdiagnosis rates, delayed responses, and limited scalability, which are increasingly incompatible with the demands of modern smart agriculture. Using the “Agricultural Security Identification and Prevention” crop disease and pest identification system as the research carrier, this study systematically analyzes the application mechanism of integrating the Internet of Things and artificial intelligence for real-time crop health monitoring. By reviewing the development status of pest and disease identification technologies domestically and internationally, the work clarifies current technical gaps and application challenges. On this basis, a comprehensive system architecture is designed, covering hardware acquisition devices, lightweight image recognition algorithms, data management ecology, and diversified application scenarios for field and mobile use. Experimental and pilot application results demonstrate that the system achieves a detection accuracy of at least 90% and a response time within 3 seconds through dual-mode operation on mobile phone and dedicated hardware terminals, while reducing pesticide use costs by approximately 20%–30%. Furthermore, the paper discusses the industrialization pathway, scalability, and future development trends of intelligent pest identification technology, providing a technical reference and practical paradigm for advancing smart agriculture and supporting rural revitalization.

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