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All Journal Jurnal Ilmiah Mikrotek Jurnal Simantec REKAYASA Jurnal Otomasi Kontrol dan Instrumentasi Journal of Robotics and Control (JRC) JAREE (Journal on Advanced Research in Electrical Engineering) Journal of Technology and Informatics (JoTI)
Muhammad Fuad
Fakultas Teknik, Universitas Trunojoyo
Aerospace Engineering Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Physics Other

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Brush-shaped Motion Gesture of UGV Using Hand Gesture Recognition Agus Murdiono Agus; 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 Murdiono Agus Ahmad Sahru Romadhon Alfian Ma’arif Andharini Dwi Cahyani Darwaman, Mochammad Ilham Diah Kusumawati Fadzillah Akbar Subkhi Faikul Umam Hairil Budiarto Laksono, Deni Tri Maulana, Fahmi Nuniek Fahriani, Nuniek Nurwahyudi, Ilham Setiawan, Fachruddin Ari Siti Nurjanah Sri Wahyuni Sri Wahyuni Sudianto, Achmad Imam Suwarno, Iswanto Sya’diyah, Lailatus Trihastuti Agustinah Trihastuti Agustinah Umam, Khanif Khoirul Vivi Tri Widyaningrum Wahyu Caesarendra