Peer-Reviewed Publication
J Insect Sci2026;26(5)September 1, 2026Journal Article
Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.
Steve B S Baleba1,2, Victor O Omondi1,2, Pascal Aigbedion-Atalor3,4, Emmanuel Peter1,5, Souleymane Diallo1, Komi Mensah Agboka1,6
1Behavioural and Chemical Ecology Unit, International Centre of Insect Physiology and Ecology (icipe), Duduville, Kasarani, Nairobi, 30772-00100, Kenya.
2Department of Zoology and Entomology, University of Pretoria, Hatfield, South Africa.
3Department of Plant and Environmental Protection Sciences, College of Tropical Agriculture and Human Resilience, University of Hawaii at Manoa, Honolulu, HI, USA.
4College of Tropical Agriculture and Human, Resilience, University of Hawaii at Manoa, Komohana Center for Applied Research and Extension Services, Hilo, HI, USA.
5Faculty of Agriculture, Federal University Gashua, Gashua, Yobe State, Nigeria.
6Laboratoire de Recherche en Science et Technologie (LARSI), Département de Génie Informatique (GI), École Polytechnique de Lomé (EPL), Université de Lomé, Lomé, Togo.
Abstract
Machine learning is transforming chemical ecology by accelerating the discovery and deployment of semiochemical-based tools for precision pest management. These advances are particularly important in the face of climate change, pesticide resistance, and the growing need for sustainable agricultural intensification. This review synthesizes how machine learning can be applied across the semiochemica…
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