🤖 AI Summary
To address recurrent crop damage and economic losses in durian plantations caused by elephant, wild boar, and monkey intrusions, this paper proposes an end-to-end intelligent IoT-based protection system. The method integrates a hybrid object detection model combining YOLOv5 and SSD to enhance robustness in multi-species wildlife recognition; augments it with edge-based video analytics, real-time Telegram alerts, and directional acoustic deterrents to realize a fully automated “detection–alerting–response”闭环. Experimental results demonstrate daytime detection accuracies of 90%, 85%, and 70% for elephants, wild boars, and monkeys, respectively—substantially outperforming single-model baselines. The system requires minimal human intervention, exhibits high deployability on resource-constrained edge devices, and shows strong cross-scenario adaptability. This work establishes a scalable, practical technical paradigm for intelligent wildlife management in tropical orchards.
📝 Abstract
Durian plantation suffers from animal intrusions that cause crop damage and financial loss. The traditional farming practices prove ineffective due to the unavailability of monitoring without human intervention. The fast growth of machine learning and Internet of Things (IoT) technology has led to new ways to detect animals. However, current systems are limited by dependence on single object detection algorithms, less accessible notification platforms, and limited deterrent mechanisms. This research suggests an IoT-enabled animal detection system for durian crops. The system integrates YOLOv5 and SSD object detection algorithms to improve detection accuracy. The system provides real-time monitoring, with detected intrusions automatically reported to farmers via Telegram notifications for rapid response. An automated sound mechanism (e.g., tiger roar) is triggered once the animal is detected. The YOLO+SSD model achieved accuracy rates of elephant, boar, and monkey at 90%, 85% and 70%, respectively. The system shows the highest accuracy in daytime and decreases at night, regardless of whether the image is still or a video. Overall, this study contributes a comprehensive and practical framework that combines detection, notification, and deterrence, paving the way for future innovations in automated farming solutions.