Smart IoT-Based Wildlife Detection & Crop Protection System

Agricultural productivity is significantly impacted by wildlife intrusion and unfavourable environmental conditions, necessitating the integration of modern technology to enhance crop protection and growth monitoring. This project presents an intelligent IoT and sensor-based automation system designed to safeguard crops while ensuring optimal growth conditions. The system incorporates multiple sensors to monitor environmental parameters and detect potential threats in real time. A soil moisture sensor continuously assesses soil moisture levels, and when low moisture is detected, it automatically activates a water motor to initiate irrigation, preventing water stress in crops. Additionally, a DHT sensor monitors temperature and humidity, providing farmers with real-time data updates through a mobile IoT platform, enabling proactive decision-making for maintaining ideal growing conditions. To mitigate the risks posed by wildlife intrusion, a Passive Infrared (PIR) sensor is deployed to detect motion in the field. Upon detecting movement, the system triggers an alarm buzzer to deter animals and simultaneously updates the IoT platform, alerting farmers about potential threats. Furthermore, the security aspect is enhanced using image processing techniques based on MATLAB and Convolutional Neural Networks (CNN). The system captures and processes images of intruding wildlife, accurately identifying animal species and automatically triggering an alert system for immediate intervention. By integrating IoT with machine learning-based image recognition, the system offers a dual-layered security mechanism, ensuring a proactive response to wildlife threats.