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All Journal Journal of Technology and Informatics (JoTI)
Achmad Imam Sudianto
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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A Real-Time Human-Drone Interaction System for Cornfield Perimeter Monitoring Using Hand Gesture Control Fadzillah Akbar Subkhi; Muhammad Fuad; Sri Wahyuni; Achmad Imam Sudianto; Tri Widyaningrum, Vivi; Ach. Dafid
Journal of Technology and Informatics (JoTI) Vol. 8 No. 1 (2026): Vol. 8 N. 1 (2026)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v8i1.1314

Abstract

Perimeter monitoring in agricultural fields is essential for maintaining security and ensuring continuous observation of field conditions. This study develops a real-time human–drone interaction system using hand-gesture recognition based on MediaPipe Hands and a Support Vector Machine (SVM) classifier. A custom dataset of 24,000 images across 12 gesture classes was collected and converted into 42 hand landmarks (x, y, z), normalized relative to the wrist point. The SVM model with an RBF kernel was trained using an 80:20 split and achieved a testing accuracy of 99.18%. The system operates at 109 FPS with an average latency of 9.16 ms, enabling rapid and reliable drone responses to gesture commands. Field testing in a cornfield with FPV camera visualization demonstrated that the system consistently recognized gestures in varying outdoor lighting, allowing drones to execute precise perimeter checks and maneuvers. These results highlight the significant potential of integrating gesture recognition with drone control, providing a practical, real-world solution that advances smart farming, increases agricultural efficiency, and supports technological progress toward Sustainable Development Goals. The proposed system thus offers a lightweight, responsive, and impactful tool for modern agricultural perimeter monitoring.
Brush-shaped Motion Gesture of UGV Using Hand Gesture Recognition Agus, Agus Murdiono; Muhammad Fuad; Hairil Budiarto; Faikul Umam; Vivi Tri Widyaningrum; Achmad Imam Sudianto
Journal of Technology and Informatics (JoTI) Vol. 8 No. 1 (2026): Vol. 8 N. 1 (2026)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v8i1.1315

Abstract

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.
Co-Authors Ach. Dafid Agus, Agus Murdiono Fadzillah Akbar Subkhi Faikul Umam Hairil Budiarto MUHAMMAD FUAD Sri Wahyuni Vivi Tri Widyaningrum Vivi Tri Widyaningrum, Vivi