This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
David Sunday ARAOTI,,
- Independent Researcher, Oyo State,, , nigeria
Abstract
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; entomology; machine learning; pest management; biodiversity monitoring; precision agriculture; ecological modeling.
David Sunday ARAOTI,. Artificial Intelligence in Entomology: Global Advances, Applications, and Future Directions in Insect Research and Pest Management. International Journal of Insects. 2026; 03(01):-.
David Sunday ARAOTI,. Artificial Intelligence in Entomology: Global Advances, Applications, and Future Directions in Insect Research and Pest Management. International Journal of Insects. 2026; 03(01):-. Available from: https://journals.stmjournals.com/iji/article=2026/view=239652
References
1. Valan M, Makonyi K, Maki A, Vondráček D, Ronquist F. Automated insect identification with deep learning: challenges and opportunities. Ecol Inform. 2021; 61:101251.
2. Carrasco J, et al. AI applications in pest management and ecological modeling. J Pest Sci. 2020;93(3):1005-1018.
3. Kellenberger B, Marcos D, Tuia D. Computer vision for ecological monitoring. Methods Ecol Evol. 2019;10(9):1444-1456.
4. Zhang Z, et al. Deep learning for biodiversity research: applications and perspectives. Ecol Appl. 2022;32(4): e2527.
5. United Nations. Sustainable development goals [Internet]. New York: United Nations; 2015 [cited 2026 Mar 9]. Available from: https://sdgs.un.org/goals
6. Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015; 61:85-117.
7. Potamitis I, Rigakis I, Tatlas NA. Insect monitoring using acoustic sensors and AI. Sensors (Basel). 2019;19.
8. Chiu MC, et al. AI-assisted pest detection and its impact on sustainable agriculture. Front Sustain Food Syst. 2021; 5:631497.
9. Wäldchen J, Mäder P. Machine learning for image-based species identification. Methods Ecol Evol. 2018;9(11):2216-2225.
10. Viraktamath S, et al. Drone-based monitoring of agricultural pests: AI applications. Precis Agric. 2022;23(6):1531-1548.
11. Ferreira C, et al. Artificial intelligence in conservation biology: current uses and perspectives. Biol Conserv. 2021; 257:109076.
12. Van Huis A. Insects as food and feed: a new AI-driven frontier. J Insects Food Feed. 2020;6(4):353-361.
13. Gómez C, et al. Remote sensing and AI for biodiversity monitoring: a global perspective. Ecol Indic. 2020; 115:106400.
14. Banza P, et al. Ethical challenges in applying AI to ecology and agriculture. AI Soc. 2022;37(3):1245-1258.
15. Liu Y, et al. Internet of Things and AI for precision pest management. Comput Electron Agric. 2023; 206:107657
International Journal of Insects
| Volume | 03 |
| 01 | |
| Received | 24/09/2025 |
| Accepted | 07/03/2026 |
| Published | 15/03/2026 |
| Publication Time | 172 Days |
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