Convolutional neural network analysis of Diaphorina citri (Hemiptera: Liviidae) vibrational communication signals for enhanced mating disruption.

Authors

R W Mankin, S McNeill, C McNeill

Published in

Journal of economic entomology. Jun 19, 2026. Epub Jun 19, 2026.

Abstract

Diaphorina citri Kuwayama (Hemiptera: Liviidae) vector bacteria (Candidatus Liberibacter asiaticus) that cause huanglongbing, an economically devastating disease of citrus. Management of D. citri infestations is primarily through insecticides, but alternative control methods remain under consideration including the co-opting and disruption of D. citri mating-duet vibrational communication signals. This study applies generative adversarial network and convolutional neural network methods to distinguish among female and male duetting signals when the sex of the signaler is not visually verifiable or when multiple male-female pairs are duetting. Up to 99% accuracy was achieved in identifying D. citri female and male signalers in experiments where standard statistical analyses fail to distinguish them at P < 0.05 statistical significance. Such knowledge has potential to increase mating disruption effectiveness by identifying signals to which females have greater preference. Although these studies were conducted only with D. citri, there is future opportunity to consider interference from communication signals produced when vibrational signals of multiple pest species are present and, combined with Internet of Things (IoT) technological capabilities, to further improve the capability for early detection and management of insect pests.

PMID:
42320042
Bibliographic data and abstract were imported from PubMed on 20 Jun 2026.

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