Integrating artificial intelligence with classical entomology: Enhancing insect biodiversity monitoring for sustainable agriculture in Iran

Insect biodiversity is a cornerstone of ecosystem resilience and agricultural sustainability, contributing to pollination, pest regulation, and nutrient cycling. Traditional entomological methods, while foundational, face limitations in scalability, speed, and accessibility. The integration of Artificial Intelligence (AI)-particularly machine learning and computer vision-offers transformative capabilities for automated insect identification and real-time monitoring. This paper presents a comprehensive review of AI applications in entomology, emphasizing their relevance to Iran's diverse agroecological zones. We explore global advancements, assess regional challenges such as data scarcity and infrastructure gaps, and propose strategic frameworks for interdisciplinary collaboration. By leveraging AI alongside classical taxonomy, Iran can enhance pest management, conserve biodiversity, and advance precision agriculture. Notably, the development of an AI-based software system for insect systematics and automatic generation of identification keys, currently in the process of patent registration by the lead author, represents a pioneering innovation in this field. Future directions include developing annotated datasets, deploying smart traps and drones, and fostering Al literacy among entomologists and farmers.

کلیدواژه ها:

Artificial intelligence ، Entomology ، Image classification ، Pest monitoring ، Predictive modeling