Artificial Intelligence Driven Insect Pest and Disease Management: Innovations, Challenges and Future Prospects

This paper reviewed the current applications of AI in insect pest management and critically examined associated challenges such as limited availability of high-quality datasets, variability in field conditions, and restricted model generalization across crops and agro-ecological regions. Insect pests represented a major constraint to global agricultural productivity, food security, and ecosystem stability. Conventional pest management approaches largely depended on manual field surveillance and non-selective chemical control, which were often labour-intensive, environmentally hazardous, and limited in precision. These traditional methods also frequently resulted in delayed detection and excessive pesticide application, further aggravating environmental and economic concerns. Recent advances in Artificial Intelligence (AI) provided innovative opportunities to overcome these limitations by enabling automated pest detection, accurate species identification, outbreak forecasting, and real-time decision support. AI-based technologies, including machine learning, deep learning, computer vision, remote sensing, and Internet of Things (IoT) platforms, have significantly improved the efficiency and reliability of pest monitoring and management systems. These tools facilitated early warning, site-specific interventions, and optimized resource utilization, thereby supporting sustainable pest control strategies. In addition, AI-driven analytics supported data integration from multiple sources, enhancing predictive accuracy and adaptive management approaches. Future prospects, including the integration of explainable AI, autonomous precision robotics, and AI-driven Integrated Pest Management (IPM) frameworks, were also discussed. The effective incorporation of AI into pest management systems had substantial potential to reduce pesticide dependence, enhanced crop health, and improved resilience to climate-induced shifts in pest populations, contributing to the advancement of sustainable and climate-smart agriculture.