Real-Time IoT-Based Monkey Pest Detection Using YOLOv8 for Shallot Farming - Deny Rochman Arifatno; Debyo Saptono; M Nur Rois Abid; Susan Siti Nurhaliza (LPPM Universitas Islam 45 Bekasi, 2026)
Shallot farming is a high-value horticultural sector vital for national food security. Farmers in forest-edge areas face serious losses from monkey pests (Macaca spp.), which can cause rapid, large-scale crop damage. Conventional manual and reactive control methods, such as direct guarding or simple repellents, are often ineffective and unsustainable. This study proposes an IoT-based real-time monkey pest monitoring system using a camera sensor and Raspberry Pi. The system automatically detects monkey presence using the YOLOv8 object detection model and immediately alerts farmers via mobile devices, while activating a buzzer or speaker alarm to deter the animals. The research stages include user needs analysis, system design and implementation with Raspberry Pi 5 as the central processor, field testing, and performance evaluation. The model was trained on approximately 2000 labeled monkey images and achieved 86.3% precision, 85.3% recall, and 90.5% mAP@50. In real-time operation, the system runs at 18–22 frames per second with an overall detection accuracy of 82% and a false positive rate of 8%. The system can distinguish monkeys from humans in the same frame, providing an effective early warning tool for shallot plantations.