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From the left, the research team of Yihong Feng, Swarna Sethu, and Dongyi Wang.
The National Science Foundation awarded an Innovation Corp entrepreneurship grant to a research team at the Arkansas Agricultural Experiment Station to develop a machine vision system developed using artificial intelligence guided imagery.
A $50,000 grant supports participation in NSF’s I-Corps program. According to the NSF’s I-Corp website, technology developers move promising ideas and technologies from the lab to the marketplace to help increase U.S. economic competitiveness and promote cooperation between academia and industry. Designed.
The Department of Bioagricultural Engineering’s team includes Assistant Professor Wang Dongyi as a principal investigator. His Swarna Sethu, a postdoctoral fellow, as an entrepreneurial leader. Graduate student Yihong Feng as technical lead. Wale Obadimu, Site Reliability Engineer at LinkedIn, is an industry leader.
Sethu said the NSF I-Corps program uses experiential education to help researchers gain valuable insight into starting entrepreneurship, business or industry requirements and challenges. Attendees will learn practical skills to connect with customers, ask the right questions, find partners and get their startup ideas off the ground.
As part of the I-Corps program, Sethu participated in a 7-week cohort program. This is a training seminar to develop your entrepreneurial skills. She interviewed industry advisors and consumers to help determine the most promising markets for this technology.
Funded by an NSF seed grant, Wang developed the idea of teaching an AI program to identify human responses to digital images of food. The team used machine learning technology to train an AI-guided digital imaging system to predict whether consumers would accept the food.
Sethu photographed the product under different colored lights to alter the appearance of the food.
The team collaborated with Han-Seok Seo, an associate professor of sensory science in the Food Science Department at the Experiment Station, to compare machine predictions with a consumer panel of people trained to evaluate foods in his lab. associated with. Sethu said 75 panelists participated in the study.
According to Wang, AI-guided systems are now highly reliable in predicting consumer acceptance. “We may have hard data to support that prediction,” he said.
Wang said the system will also help consumers shopping on retailers’ mobile apps by accurately displaying product images in the most appealing lighting. “We can create an enhanced visual experience for consumers.”
According to Wang, the forecasting technology is almost complete, but a marketable app needs to be developed. He said the team might also investigate how the technology could be adapted to other retail products.
For more information about the Agricultural Research Division, visit the Arkansas Agricultural Experiment Station website (https://aaes.uada.edu/). Follow us on Twitter. @ArkAgResearch and on Instagram @ArkAgResearch. For more information on the agriculture sector, please visit https://uada.edu/. Follow us on Twitter. @AgInArk.
About the agricultural sector: The mission of the University of Arkansas Systems Agriculture Division is to strengthen agriculture, communities, and families by linking sound research with the adoption of best practices. Through the Agricultural Experiment Station and Joint Extension Service, the agricultural sector conducts research and extension work within the country’s historic land grant education system. The Department of Agriculture is one of 20 entities within the University of Arkansas system. It has offices in all 75 Arkansas counties and faculties on five system campuses. The University of Arkansas School of Agriculture recognizes race, color, sex, gender identity, sexual orientation, national origin, religion, age, disability, marital or veteran status, genetic information, or other legally protected status. and is an affirmative action/equal opportunity employer.
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