UTTAR PRADESH JOURNAL OF ZOOLOGY

Agriculture forms the foundation of India’s economy, supporting nearly 70% of the rural population. However, pest infestations cause significant yield and economic losses, necessitating sustainable pest management strategies. Conventional chemical control practices have led to pest resistance, residue accumulation, and environmental degradation. Recently, Artificial Intelligence (AI) has emerged as a transformative tool in Integrated Pest Management (IPM). Machine learning and deep learning algorithms, such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), have demonstrated high precision in pest detection and classification — with certain CNN-based models achieving up to 96% accuracy in identifying crop pests and disease symptoms, and SVM-based approaches reporting over 90% prediction accuracy in pest population forecasting. Integration of AI with Internet of Things (IoT)-enabled smart traps, drones, and mobile platforms like Plantix, Agrio, and Nuru has enhanced real-time monitoring and precision decision-making in pest control. Despite challenges such as limited datasets and low digital literacy, AI-driven IPM represents a sustainable, eco-friendly, and data-informed solution for resilient agricultural systems. This review highlights the applications, advancements, and challenges of AI-based IPM, emphasizing its potential to revolutionize sustainable and precision pest management for future-ready agriculture.