<![CDATA[Manual observation of corn leaf diseases in agricultural fields often faces challenges related to time, effort, and accuracy. To address these challenges, brush-shaped motion patterns, such as zig-zag and boustrophedon trajectories, provide an effective solution by enabling uniform area coverage while reducing redundant traversal, energy consumption, and sensing gaps, making them well-suited for precision agriculture applications. Building on this approach, the system utilizes the MediaPipe framework for hand landmark tracking and the K-Nearest Neighbors (KNN) algorithm to recognize six navigation commands: forward, backward, stop, turn_right, turn_left, and capture. These commands are transmitted via Wi-Fi with an average latency of 0.001964 s. To ensure navigation accuracy during pattern execution, corrections are made using rotary encoders. Gesture classification experiments on 6,000 samples achieved a maximum accuracy of 99.46% across two participants, with stable KNN performance under both indoor and outdoor lighting variations, as well as hand distances ranging from 50 cm. Furthermore, the capture gesture produced an average image acquisition latency of 0.3037 s at various UGV observation positions. In summary, these results demonstrate that integrating real-time gesture control with UGV maneuvers enables systematic field surveys for maize leaf disease monitoring and supports Sustainable Development Goal (SDG) 2 through precision agriculture technology.]]>
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