딥러닝 기반 감수지 액적 자동 인식 시스템 개발

초록

This study aimed to develop a real-time, drone-based pesticide spraying performance evaluation systemapplicable in field conditions. To achieve robust detection performance across domain discrepancies and noise, weemployed self-supervised learning techniques. The training dataset was collected through a drone spraying testdesigned to capture droplets on water-sensitive paper and comprised processed ground-truth data and field datacaptured under various environmental conditions. For practical use in real-world applications, we adopted alightweight model that can be used in embedded computers. Comparative testing with varied environmentalspraying datasets showed that the proposed system demonstrated greater robustness in detecting droplets under diverse, irregular field conditions. With continued research, this system is expected to evolve to deliver even higher detection precision and adaptability across varied environment.

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