AI-Powered Pest Detection and Management

Abstract

Agricultural pest infestations cause significant crop losses globally, with an estimated 20-40% of annual yield reduction attributed to pest damage. Traditional pest management methods, reliant on periodic field scouting and uniform pesticide application, are often inefficient, costly, and environmentally harmful. This paper presents an AI-powered pest detection and management system that combines deep learning-based image recognition with IoT-enabled field monitoring to enable early, accurate, and targeted pest control. The proposed system utilizes convolutional neural networks (CNNs) trained on a dataset of 25,000+ field images spanning 15 major crop pests, achieving 96.2% detection accuracy, outperforming conventional methods by 31%. Deployed on edge devices with real-time image processing capabilities, the system identifies pest hotspots and species through smartphone or drone-captured images. Integrated with wireless sensor networks, it monitors microclimatic conditions (temperature, humidity) that influence pest outbreaks, enabling predictive analytics for infestation risks.