AI-Driven Pest and Disease Detection in Smart Farming Systems

Abstract

AI-driven pest and disease detection is transforming agriculture by enabling precise and early identification of crop health issues. This paper explores the integration of Artificial Intelligence (AI), specifically machine learning (ML) and deep learning (DL), for pest and disease management in smart farming systems. By utilizing real-time data from IoT sensors, drones, and satellite imagery, AI models can detect crop diseases and pests early, enabling targeted interventions. The paper reviews existing AI-based systems and proposes a framework that combines image processing, machine learning, and environmental data to enhance pest detection and reduce pesticide usage. The study concludes that AI can improve crop yield, reduce environmental impact, and promote sustainable farming practices.