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Insect vector feeding recognized by machine learning

Date:
November 10, 2016
Source:
PLOS
Summary:
Scientists have used machine learning algorithms to teach computers to recognize the insect feeding patterns involved in pathogen transmission. The study also uncovers plant traits that might lead to the disruption of pathogen transmission and enable advances in agriculture, livestock and human health.
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Scientists have used machine learning algorithms to teach computers to recognize the insect feeding patterns involved in pathogen transmission. The study, published in PLOS Computational Biology, also uncovers plant traits that might lead to the disruption of pathogen transmission and enable advances in agriculture, livestock and human health.

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. These insect vectors can acquire and transmit pathogens causing infectious diseases such as citrus greening through probing on host tissues and ingesting host fluids. 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.

In this research, entomologists and computer scientists at the United States Department of Agriculture-Agricultural Research Service (USDA-ARS), University of Florida, and Princeton University used machine learning algorithms to teach computers to recognize insect feeding patterns involved in pathogen transmission.

In addition, these machine learning algorithms were used to detect novel patterns of insect feeding and uncover plant traits that might that lead to disruption of pathogen transmission. While these techniques were used to help identify strategies to combat citrus greening, such intelligent monitoring of insect vector feeding will facilitate rapid screening and disruption of pathogen transmission causing disease in agriculture, livestock, and human health.


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Journal Reference:

  1. Denis S. Willett, Justin George, Nora S. Willett, Lukasz L. Stelinski, Stephen L. Lapointe. Machine Learning for Characterization of Insect Vector Feeding. PLOS Computational Biology, 2016; 12 (11): e1005158 DOI: 10.1371/journal.pcbi.1005158

Cite This Page:

PLOS. "Insect vector feeding recognized by machine learning." ScienceDaily. ScienceDaily, 10 November 2016. <www.sciencedaily.com/releases/2016/11/161110153224.htm>.
PLOS. (2016, November 10). Insect vector feeding recognized by machine learning. ScienceDaily. Retrieved May 24, 2017 from www.sciencedaily.com/releases/2016/11/161110153224.htm
PLOS. "Insect vector feeding recognized by machine learning." ScienceDaily. www.sciencedaily.com/releases/2016/11/161110153224.htm (accessed May 24, 2017).

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