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.

以摄取动植物体液为食的昆虫，主要通过传播动植物病原体，对全球人类、家畜及农业造成毁灭性危害。刺吸式昆虫成功传播病原体所需的取食过程，可通过监测昆虫-食源饲喂回路中的电压变化进行记录。传统上，此类监测得到的输出数据需人工分析，这一过程缓慢且繁重。我们研发了一款计算机程序，可自动分类此前已被报道的、与柑橘黄龙病病原体传播相关的昆虫取食模式。此外，我们还展示了此类分析如何助力发现此前未被认知的取食状态，并可用于表征植物的抗病机制。这一技术进展大幅缩短了昆虫取食分析所需的时间与人力投入，将有助于开发、筛选及测试新型干预策略，以阻断影响农业、家畜及人类健康的病原体传播。