用混合模型+物联网，自动识别并驱赶闯入榴莲园的动物
A Hybrid YOLOv5-SSD IoT-Based Animal Detection System for Durian Plantation Protection
- 融合YOLOv5与SSD提升检测精度
- 对大象、野猪、猴子识别率分别达90%、85%、70%
- 支持实时报警和自动播放威慑音效，适合农业安防场景
榴莲园常遭动物侵扰导致作物损失和经济受损，传统人工监控方式难以持续。随着机器学习与物联网（IoT）技术的发展，新型检测方案应运而生。然而现有系统受限于单一目标检测算法、通知平台不普及及缺乏有效驱赶机制。本研究提出一种基于IoT的动物检测系统，融合YOLOv5与SSD目标检测算法以提升准确性。系统实现实时监控，一旦发现入侵者，通过Telegram即时通知农户以便快速响应；同时触发自动化声学驱赶机制（如虎啸声）。在白天，对大象、野猪和猴子的检测准确率分别达到90%、85%和70%，夜间性能有所下降，无论图像或视频均呈现此趋势。该研究构建了一个集检测、通知与驱赶于一体的完整实用框架，为智能农业防护提供新范式。
原文摘要 · Abstract (English)
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.
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