| 英文摘要 |
With rapid urbanization, large-scale migration to cities has led to a concentration of food sources and suitable habitats, providing ideal conditions for insects to breed and thrive. In recent years, climate change has significantly
impacted the types and distribution of urban pests. Effective pest control relies on the ability to accurately and automatically identify pests, as different species require different control methods. Advanced image recognition technology, powered by artificial intelligence (AI), enables the rapid and accurate identification of various pests.This project developed image recognition models for 14 common urban pests. After training and testing with labeled image datasets, the models achieved an identification accuracy of over 0.9. Additionally, a six-month survey was conducted at 10 locations in western Taiwan using sticky traps and flypaper. The results indicated that latitude variation and the type of bait used in sticky traps, rather than environmental type, were the primary factors affecting the composition of arthropods in indoor environments. The species diversity in southern Taiwan was significantly higher than in northern and central regions, influenced by latitude-related climatic conditions. The project also analyzed application models for smart insect identification systems in residential settings. Testing revealed varying performance and cost-effectiveness across different devices. Lightweight edge devices achieved sufficient accuracy at low computational costs. Integrating this system into mobile apps or web platforms could provide practical pest identification tools for the general public. Additionally, plans are underway to create a household insect database, which will include identification features, biological information, and pest control recommendations for common arthropods. Through multi-scenario applications, this initiative aims to enhance the effectiveness and user experience of insect identification systems.
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