Machine Learning for Characterization of Insect Vector Feeding.
Insects that feed by ingesting plant and animal fluids cause devastating damage to humans, livestock, and agriculture worldwide, primarily by transmitting pathogens of plants and animals. The feeding processes required for successful pathogen 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. We taught 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.
USDA-ARS, Chemistry Unit, Center for Medical, Agricultural, and Veterinary Entomology, Gainesville, FL, USA.
Denis S
DS
Willett
USDA-ARS, Subtropical Insects and Horticultural Research Unit, United States Horticultural Research Laboratory, Fort Pierce, Florida, USA.
Justin
J
George
Department of Computer Science, Princeton University, Princeton, NJ, USA.
Nora S
NS
Willett
University of Florida, Entomology and Nematology Department, Citrus Research and Education Center, University of Florida, Lake ALfred, FL, USA.
Lukasz L
LL
Stelinski
USDA-ARS, Subtropical Insects and Horticultural Research Unit, United States Horticultural Research Laboratory, Fort Pierce, Florida, USA.
Stephen L
SL
Lapointe
D000818:Animals; D002957:Citrus / Q000469:parasitology*; D003198:Computer Simulation; D005247:Feeding Behavior / Q000502:physiology*; D007303:Insect Vectors / Q000502:physiology*; D000069550:Machine Learning; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D010992:Plethysmography, Impedance / Q000379:methods; D013391:Sucking Behavior / Q000502:physiology