Artificial Intelligence in Entomology: Global Advances, Applications, and Future Directions in Insect Research and Pest Management - David Sunday Araoti (International Journal of Insects, 2026)
Artificial Intelligence (AI) is transforming entomology by enabling scalable, data-driven approaches to insect identification, ecological monitoring, and sustainable pest management. This review synthesizes recent global advances in AI applications across taxonomy, behavioral ecology, predictive modeling, and precision agriculture. Machine learning and deep learning techniques—including convolutional neural networks, acoustic classification models, and ensemble predictive algorithms—have demonstrated high classification accuracies (often exceeding 90% under controlled conditions) and improved early detection of pest outbreaks. Integration with Internet of Things (IoT) sensors, drones, and remote sensing platforms enables real-time ecological intelligence and decision support. Quantitative evidence suggests that AI- assisted pest management systems can reduce pesticide usage by 20–40% in precision agriculture contexts while improving intervention timing. However, challenges remain, including dataset bias, limited interpretability of deep learning models, infrastructure disparities, and ethical concerns regarding automated ecological interventions and data governance. This review proposes a structured theoretical framework for AI-enabled entomology, summarizes methodological approaches, evaluates empirical outcomes, and identifies scalable and ethically responsible pathways for future research and implementation.
Keywords: Keywords: artificial intelligence; Machine Learning; Precision agriculture; biodiversity monitoring; ecological modeling; entomology; pest management