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ARS Home » Southeast Area » Raleigh, North Carolina » Soybean and Nitrogen Fixation Research » Research » Publications at this Location » Publication #375748

Research Project: Exploiting Genetic Diversity through Genomics, Plant Physiology, and Plant Breeding to Increase Competitiveness of U.S. Soybeans in Global Markets

Location: Soybean and Nitrogen Fixation Research

Title: Drought stress detection using low-cost computer vision systems and machine learning techniques

item RAMOS-GIRALDO, PAULA - North Carolina State University
item REBERG-HORTON, CHRIS - North Carolina State University
item Locke, Anna
item Mirsky, Steven
item LOBATON, EDGAR - North Carolina State University

Submitted to: IEEE IT Professional
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/27/2020
Publication Date: 5/21/2020
Citation: Ramos-Giraldo, P., Reberg-Horton, C., Locke, A.M., Mirsky, S.B., Lobaton, E. 2020. Drought stress detection using low-cost computer vision systems and machine learning techniques. IEEE IT Professional.

Interpretive Summary: Visual assessments of wilting are still critical indicators of crop drought stress for on-farm and research applications. The automation of these assessments could improve precision irrigation technologies and increase the throughput of crop phenotyping for breeding applications. Here, cameras were built using low-cost, commercially available parts. Images from these cameras were used to train machine learning (ML) models to evaluate leaf wilting. Using ML, the computer was able to match expert human ratings with over 80% accuracy.

Technical Abstract: The real-time detection of drought stress has major implications for preventing cash crop yield loss due to variable weather conditions and ongoing climate change. The most widely used indicator of drought sensitivity/tolerance in corn and soybean is the presence or absence of leaf wilting during periods of water stress. We develop a low-cost automated drought detection system using computer vision coupled with machine learning (ML) algorithms that document the drought response in corn and soybeans field crops. Using ML, we predict the drought status of crop plants with more than 80% accuracy relative to expert-derived visual drought ratings.