SU, YU JUI
National Pingtung Senior High School Student
Email：edwardsu5416@gmail.com
WU, ZHENG-YUAN
National Pingtung Senior Industrial Vocational SchoolStudentof Electrical Engineering
Email：a7621269@gmail.com
CHEN, YOURONG
National Pingtung Senior High School Student
Email：chenyourong0219@gmail.com
Abstract
This research addresses the problem of locust disasters caused by locust outbreaks by proposing an automated locust monitoring and control system based on machine vision and deep learning algorithms. The system uses cameras to capture on-site images, employing image recognition technology to accurately identify locusts and other related insects, and it determines the optimal control timing based on the frequency and distribution characteristics of locust occurrences. Experimental results indicate that the system meets commercial application standards in terms of locust recognition accuracy, real-time responsiveness, and system stability. Additionally, the system can seamlessly integrate with existing automatic humidityadjustment and insect control equipment, effectively enhancing the efficiency of locust disaster control. Future research will extend towards edge computing, low-power design, and multi-sensor fusion to tackle increasingly complex and dynamic locust disaster control needs.
Keywords: Insect monitoring, automated control, smart agriculture, agricultural applications, pest issues, food crisis.