AI-Driven Animal Intrusion Detection System for Agriculture: Real-Time Monitoring and Automated Response Using Deep Learning

K.Subrahmanyam; Bandili Pavani; Ammuru Ramyasri; Meejuru Kishore; Malli Kumar; Koduru Suresh

AI-Driven Animal Intrusion Detection System for Agriculture: Real-Time Monitoring and Automated Response Using Deep Learning

Authors: K.Subrahmanyam, Bandili Pavani, Ammuru Ramyasri, Meejuru Kishore, Malli Kumar, Koduru Suresh

Unique Paper ID: 178105
Volume: 11
Issue: 12
PageNo: 2197-2201

Keywords: ESP32-CAM_Module ESP32-Micro Controller AI- Driven Detection Real-Time Alert System

Abstract:
The AI-Driven Animal Intrusion Detection System is an advanced solution designed to monitor agricultural fields, detect animal intrusions in real-time, and trigger automated deterrents. This system helps protect crops, reduce manual surveillance efforts, and enhance field security using deep learning and IoT technology. The system utilizes an ESP32 microcontroller as the central processing unit, interfacing with an ESP32-CAM module for live video streaming, a pre-trained MobileNetSSD model for real-time animal detection, and Firebase for cloud-based monitoring. The ESP32-CAM captures continuous video footage of the agricultural field, and the MobileNetSSD model processes frames to detect animals such as cows, horses, birds, and other intruding species. The detection results are sent to Firebase, allowing remote monitoring and data logging. When an animal is detected, the system triggers an LED strip and buzzer as deterrents. If no intrusion is detected, the alerts remain off to conserve power. The ESP32 microcontroller retrieves data from Firebase, ensuring a seamless real-time monitoring process. The system is programmed using Python and Arduino, enabling low-latency, high-efficiency processing. A local web server facilitates wireless communication and remote access. By integrating AI-driven detection, real-time alerts, and automated deterrence, this solution enhances agricultural field management, reducing crop damage and improving security.

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ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 2197-2201

AI-Driven Animal Intrusion Detection System for Agriculture: Real-Time Monitoring and Automated Response Using Deep Learning

Available:https://ijirt.org/Article?manuscript=178105

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