Location: Crop Diseases, Pests and Genetics Research
Project Number: 2034-22000-014-022-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 1, 2023
End Date: Aug 31, 2025
Objective:
Develop and train Artificial Intelligence (AI)/Machine Learning (ML)-based software to measure waveforms from AC-DC electropenetrography (EPG) of mosquito feeding.
Approach:
AC-DC electropenetrography (EPG) unmasks otherwise invisible, piercing-sucking feeding behaviors by vectors of plant and animal pathogens. AC-DC EPG has expanded the use of EPG from tiny aphids to all sizes of arthropod, including blood-feeders and large plant-feeders like sharpshooters. Yet, EPG is not widely used because waveforms require time-consuming, manual measurement of waveforms for later statistical analysis. Thus, there is a strong need to modernize EPG software using artificial intelligence (AI)/machine-learning (ML) applications for automated waveform recognition and measurement. The work proposed herein will establish a cooperation between ARS in Parlier, CA, and Harvey Mudd College (HMC) in Claremont, CA. HMC is the number one undergraduate engineering school in the world and pioneered over 60 years ago the use of Clinic teams of undergraduate students in their Senior year to solve engineering and computer science design problems for government and private companies. A Senior Clinic composed of four or five undergraduate students majoring in Computer Science at HMC will develop and train AI/ML-based software to automatically measure mosquito feeding waveforms recorded via AC-DC electropenetrography (EPG).
