Systematic Review of AI-Driven Insect Identification and Management Strategies in Modern Farming

The rapid advancement of artificial intelligence (AI) has transformed modern farming, particularly in insect identification and management, where precision and efficiency are critical. This systematic review examines the role of AIdriven technologies in addressing pest-related challenges while improving agricultural productivity and sustainability. We analyze the integration of AI across four key dimensions: pest management, crop management, and yield improvement. Sustainable agriculture and agricultural technology adoption are discussed to assess existing research, identify trends, methodologies, and gaps in AI applications for insect detection, classification, and intervention strategies. A rigorous selection process was employed to gather relevant studies, which were evaluated based on their technical approaches, performance metrics, and practical implications. Findings reveal that machine learning and computer vision technologies dominate the field, enabling real-time insect monitoring and targeted pest control with reduced chemical usage. However, challenges such as data scarcity, model generalizability, and scalability in diverse farming environments persist. The review highlights the potential of AI to enhance decision-making in pest management while aligning with sustainable agricultural goals, though further interdisciplinary collaboration and field validation are needed to bridge the gap between research and implementation. This work provides a comprehensive foundation to guide future research and policy development in AI-driven agricultural innovation