A Hybrid YOLOv5-SSD IoT-Based Animal Detection System for Durian Plantation Protection

TL;DR

This paper presents an IoT-based animal detection system for durian plantations that combines YOLOv5 and SSD algorithms to improve detection accuracy, provide real-time alerts, and automate deterrent mechanisms, enhancing crop protection.

Contribution

It introduces a hybrid YOLOv5-SSD detection system integrated with IoT and automated deterrents, offering a practical, real-time solution for animal intrusion monitoring in agriculture.

Findings

Detection accuracy: elephants 90%, boars 85%, monkeys 70%.

System performs best during daytime, accuracy drops at night.

Automated deterrent (sound) triggered upon detection.

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.