Automated Detection and Detterents of Harmful Birds in Tomato Cultivation

This work introduces an autonomous learning system for preventing bird damage in tomato crops based on state-of-the-art computer vision and machine learning. The system applies Convolutional Neural Networks (CNN) and Random Forest (RF) for real-time bird detection and classification. Ensemble learning enhances accuracy, minimizing crop loss at the cost of being cost-effective. The research compares different methods, such as deep learning-based deterrent systems and Internet of Things (IoT) solutions. Comparison with conventional bird-scaring methods is made to show the benefits of automation. Experimental evidence confirms the effectiveness of the system and proves its potential application in precision agriculture. The improvements for the future involve the use of Unmanned Aerial Vehicles (UAVs) and IoT-based interventions to further improve scalability.