Artificial Intelligence-Based Pest and Disease Detection Systems in Precision Agriculture

Abstract

The convergence of precision agriculture and artificial intelligence (AI) has revolutionized the monitoring and management of crop health, offering transformative solutions to the perennial challenge of pest and disease outbreaks. Traditional scouting methods, characterized by their time-intensive nature and susceptibility to human error, are increasingly being superseded by AI-driven systems capable of real-time, large-scale detection. This paper explores the architecture and application of AI-based pest and disease detection systems, emphasizing the integration of computer vision, deep learning, and remote sensing technologies. By leveraging Convolutional Neural Networks (CNNs) and high-resolution imaging from drones and ground-based sensors, these systems facilitate the early identification of biotic stresses, enabling precision interventions that drastically reduce pesticide usage and operational costs. The analysis delves into the technical frameworks of data acquisition, image processing, and predictive modeling, while addressing the critical challenges of data labeling, model generalization, and real-time processing requirements. Furthermore, it highlights the transition from manual, reactive management to proactive, data-driven agricultural systems. Ultimately, AI-based detection is positioned as a pivotal component of sustainable, high-productivity agriculture, essential for mitigating food insecurity and enhancing climate resilience through optimized resource application and ecologically conscious crop protection strategies.