IoT-Based System for Humane Animal Deterrence and Sustainable Crop Management

Wildlife intrusion into the agricultural fields is a persistent challenge for farmers, leading to significant crop losses and human-animal conflicts. Conventional deterrent methods, including electric fencing and chemical repellents, often have limited efficacy, raise ethical concerns, and can harm ecosystems. In light of sustainable farming practices, there is an urgent need for innovative solutions that protect crops without harming wildlife. This paper introduces an IoT-based system designed to provide humane, eco-friendly wildlife deterrence. The system integrates motion sensors, cameras, and machine learning algorithms for real-time animal detection and analysis. Non-lethal deterrents, such as ultrasonic sound emitters, flashing lights, and motion-activated sprinklers, are deployed only when necessary, ensuring targeted responses. A mobile app interface enhances usability by offering farmers real-time monitoring, alerts, and system control from remote locations. Field trials demonstrated an 97% reduction in crop damage, validating the system's effectiveness in safeguarding crops while fostering coexistence with wildlife. The adaptive learning capability of the system ensures continuous improvement in detection accuracy and deterrent efficiency, aligning with evolving agricultural needs. This approach represents a significant step toward sustainable crop management, providing an ethical and scalable alternative to traditional wildlife control methods. The proposed system not only enhances crop yield but also promotes biodiversity by reducing harmful interventions. By integrating advanced IoT and Deep learning techniques using Convolution Neural Network (CNN) and Support Vector Machine (SVM), the accuracy predicted by CNN is 96% and SVM is 90%. The research will explore expanded deployment across diverse farming environments and integration with renewable energy sources to achieve greater scalability and sustainability.