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All Journal JuSTISe: Journal Data Science, Technology, Informatics and Security
Ocsar Hadikaryana
Universitas Kebangsaan Republik Indonesia

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Penerapan YOLOv4 dan Sensor Laser untuk Keamanan Kebun Anggur Brazil Rizky Muharam; Yasri; Ocsar Hadikaryana
Journal Data Science, Technology, Informatics and Security Vol 4 No 1 (2026): Jurnal Data Science, Technology, Informatics and Security (Juni 2026)
Publisher : Universitas Kebangsaan Republik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31848/justise.v4i1.4318

Abstract

Agricultural security is crucial for maintaining productivity and business sustainability, particularly for high-value commodities such as grapes. The threat of theft and disturbance by wild animals, such as wild boar, often causes significant losses in vineyards. This study proposes an intelligent surveillance system integrating laser sensors with the YOLOv4 (You Only Look Once) algorithm to enhance agricultural security through automated, real-time detection. The laser sensor functions as an early motion-detection mechanism, while the ESP32-CAM module captures visual imagery for subsequent analysis using the YOLOv4 model to achieve precise object classification. The system also incorporates Internet of Things (IoT) connectivity via the Blynk platform for real-time remote monitoring. Test results show that the system achieves 85% detection accuracy during daytime and 78% at night, with an average response time of one second from detection to notification.
Co-Authors Rizky Muharam Yasri