Skip to main content
ARS Home » Southeast Area » Fort Pierce, Florida » U.S. Horticultural Research Laboratory » Subtropical Insects and Horticulture Research » Research » Publications at this Location » Publication #324651

Research Project: IPM Methods for Insect Pests of Orchard Crops

Location: Subtropical Insects and Horticulture Research

Title: Machine learning for characterization of insect vector feeding

item WILLETT, D - University Of Florida
item George, Justin
item WILLETT, N - Princeton University
item STELINSKI, L - University Of Florida
item Lapointe, Stephen

Submitted to: PLoS Computational Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/20/2016
Publication Date: 10/27/2016
Citation: Willett, D.S., George, J., Willett, N.S., Stelinski, L.L., Lapointe, S.L. 2016. Machine learning for characterization of insect vector feeding. PLoS Computational Biology. 12(11):e1005158

Interpretive Summary: Insects that transmit infectious diseases do so through feeding inside their host. By connecting insects and their food source via an electrical circuit, computers learn to recognize insect feeding patterns involved in disease transmission using artificial intelligence. In addition, computers show us novel patterns of insect feeding and uncover mechanisms that lead to disruption of pathogen transmission. While we use these techniques to solve a devastating problem facing the global citrus industry, such intelligent monitoring of insect vector feeding will engender advances in disrupting transmission of diseases affecting agriculture, livestock, and human health.

Technical Abstract: Insects that feed by ingesting plant and animal fluids cause devastating damage to humans, livestock, and agriculture worldwide, primarily by transmitting phytopathogenic and zoonotic pathogens. The feeding processes required for successful disease transmission by sucking insects can be recorded by monitoring voltage changes across an insect-food source feeding circuit. The output from such monitoring has traditionally been examined manually, a slow and onerous process. Here we teach a computer program to automatically classify previously described insect feeding patterns involved in transmission of the pathogen causing citrus greening disease. We also show how such analysis contributes to discovery of previously unrecognized feeding states and can be used to characterize plant resistance mechanisms. This advance greatly reduces the time and effort required to analyze insect feeding, and should facilitate developing, screening, and testing of novel intervention strategies to disrupt pathogen transmission affecting agriculture, livestock and human health.