Accelerating AI-driven solutions for insect pest detection in Indian agriculture: A systematic review

The increasing demand for agricultural production is heavily constrained by biotic stresses such as insect pests, diseases and nematodes, which significantly reduce crop productivity. Traditional pest monitoring methods, which are often manual, time-consuming, labour-intensive and reliant on expert identification, are prone to human error and unsustainable for large-scale implementation. With the advent of digital agriculture, artificial intelligence (AI) has become a transformative tool for enhancing pest detection and management. AI-driven technologies, particularly those integrating computer vision, deep learning and machine learning, offer automated and accurate identification of pests, minimizing the misuse of chemical inputs and reducing ecological damage. Smart devices and sensor networks equipped with AI capabilities enable real-time surveillance of both biotic and abiotic stresses, promoting efficient, targeted and environmental conscious pest control strategies. This review systematically explores the historical development of AI based insect pest detection in India, highlighting their potential to enhance precision monitoring, reduce reliance on conventional practices and support sustainable crop protection. Furthermore, it addresses the key challenges associated with AI adoption in the identification of insect pests and outlines future research directions to accelerate the development and deployment of intelligent pest management systems.