Development of an AI-Based Automatic Wild Boar Capture System and Its Field Validation in Island Regions

This study proposes a stand-alone automatic wild boar capture system using artificial intelligence to reduce damage caused by wild boars. First, a data collection device using a low-cost microcontroller capable of running deep learning models with compact sensors was developed. Based on image data collected in actual field settings, a lightweight model using the YOLO algorithm was constructed. The trained model was deployed directly on a microcontroller, achieving an edge AI recognition device comparable in size to conventional trail cameras. The recognition device was integrated with existing capture mechanisms and box traps to create a selective automatic capture system that activates only when the wild boar has fully entered the trap. Furthermore, a new automatic feeder was developed to improve feeding efficiency and a compact solar power system has also been introduced, thereby achieving a fully automatic, stand-alone system independent of external power sources. Field experiments confirmed that the proposed system can accurately identify wild boars within traps and reliably execute automatic capture operations. Furthermore, the system demonstrated stable operation without issues for over a month, confirming the effectiveness of this stand-alone automatic wild boar capture system.