Data from: 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.