一种用于榴莲种植园保护的基于IoT的YOLOv5-SSD混合动物检测系统
A Hybrid YOLOv5-SSD IoT-Based Animal Detection System for Durian Plantation Protection
AI总结：
本研究提出一种结合YOLOv5和SSD的IoT动物检测系统，通过实时监控、Telegram通知和自动声音威慑机制，有效应对榴莲种植园动物入侵，对大象、野猪和猴子的检测准确率分别达90%、85%和70%。
摘要：
榴莲种植园遭受动物入侵，导致作物受损和经济损失。由于无法在无人干预的情况下进行监控，传统农业实践显得无效。机器学习和物联网（IoT）技术的快速发展带来了检测动物的新方法。然而，现有系统受限于对单一目标检测算法的依赖、不易访问的通知平台以及有限的威慑机制。本研究提出了一种用于榴莲作物的支持IoT的动物检测系统。该系统集成了YOLOv5和SSD目标检测算法以提高检测准确率。该系统提供实时监控，检测到的入侵会通过Telegram通知自动报告给农民以便快速响应。一旦检测到动物，就会触发自动声音机制（例如老虎咆哮）。YOLO+SSD模型对大象、野猪和猴子的准确率分别达到了90%、85%和70%。无论图像是静止还是视频，该系统在白天的准确率最高，在夜间有所下降。总体而言，本研究贡献了一个结合检测、通知和威慑的综合实用框架，为未来农业自动化解决方案的创新铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
