AI- and IoT-Driven Approaches for Crop Health Monitoring and Management

Abstract

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is transforming crop health management by enabling predictive, precise, and sustainable agriculture. Traditional manual and reactive monitoring methods are increasingly inadequate for addressing climate variability, pest outbreaks, nutrient deficiencies, and abiotic stresses. AI–IoT systems combine high-resolution sensors, drones, edge computing, and advanced analytics to provide continuous real-time monitoring of soil, plant, and environmental conditions. Machine learning, deep learning, and computer vision support early detection of diseases, pests, and stress while enabling data-driven decision making through predictive models and digital twins. Decision-support systems translate complex data into actionable recommendations for irrigation, nutrient management, and targeted pest control, while integration with automation and robotics improves operational efficiency. Despite significant economic and environmental benefits, adoption remains limited by connectivity gaps, costs, etc.