AI AND IOT-ENABLED REAL-TIME CROP PROTECTION: A REVIEW OF MULTI-SPECIES DAMAGE MITIGATION

Abstract

Sparrows, wild pigs, and cattle are among the animals and birds that damage crops, causing significant concern for farmers and resulting in substantial economic losses. Traditional protection methods are predominantly labor-intensive, ineffective, and lack species-specificity. This paper presents an extensive overview of intelligent crop protection systems based on Artificial Intelligence, Deep Learning, and IoT technologies. The study summarizes recent advances in object detection models, real-time monitoring systems, and species-specific prevention mechanisms. It evaluates the effectiveness of cutting-edge methods like YOLO detection, sensor fusion, and AI-driven alarms. The paper highlights existing challenges, discusses current methodologies, and proposes a system with minimal human intervention for real-time, low-cost, multi-species crop protection.