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Undergraduate Research: Deep Learning-based Plant Classifiers and Their Real-life Research Applications

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Conference

2020 ASEE Virtual Annual Conference Content Access

Location

Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Capstone, Undergraduate Research, and Projects in ECE

Tagged Division

Electrical and Computer

Tagged Topic

Diversity

Page Count

15

DOI

10.18260/1-2--35411

Permanent URL

https://peer.asee.org/35411

Download Count

60

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Paper Authors

biography

Deng Cao Central State University

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Dr. Deng Cao received his Ph.D. in Computer Science from West Virginia University in 2013. He also earned a master degree in Statistics and a master degree in Physics, both from West Virginia University. Dr. Cao currently serves as an associate professor of Computer Science at Central State University. His research interests includes Artificial Intelligence, Machine Learning, Computer Vision and Biometrics. His research has been supported by US Department of Agriculture, National Science Foundation, and US Air Force Research Laboratory.

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Cadance Lowell Central State University

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Dr. Cadance Lowell is a Professor of Agriculture at Central State University and Chair of the Department of Agricultural and Life Sciences. She received a B.S. in Botany from Duke University, a M.S. in Botany from the University of Florida, Gainesville, and a Ph.D. in Horticulture from the University of Florida, Gainesville. She did post-doctoral work with the USDA in Peoria, IL as a biochemist in soybean oligosaccharides before joining Central State University in 1989. Dr. Lowell maintains a research program in directed energy to kill weeds as an integrated pest management strategy. She mentors undergraduate students in funded research projects who have gone on to present at local, state and national conferences.

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Craig M. Schluttenhofer Central State University

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Dr. Craig Schluttenhofer received his doctorate in Plant Physiology from the University of Kentucky in 2016. In 2011, he obtained a master’s degree in Plant Pathology from Purdue University. He received bachelor's degrees in Horticulture Science as well as Plant Genetics and Breeding from Purdue University. In 2019, he joined Central State University as a research assistant professor of natural products. Dr. Schluttenhofer specializes in the genetics and biochemistry of Cannabis used for agricultural and medical purposes. He started working with hemp in 2014 while at the University of Kentucky, operating under the state hemp research pilot program. Current research foci include developing new hemp varieties, understanding the plants’ biochemistry, improving production and processing practices, and developing new uses for the crop. His research has been supported by the United States Department of Agriculture.

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Augustus Morris Central State University

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Dr. Augustus Morris is an Associate Professor of Manufacturing Engineering at Central State University, Wilberforce, OH. He is also the Program Director of the NSF funded grant, Implementing Pathways for STEM Retention and Graduation (IPSRG). His research interests include robotic applications in agriculture, haptic devices, high altitude balloon payload design, and cellulose-based composite materials.

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Austin R. Erdman Central State University

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Austin Erdman worked as a software engineer at Global Neighbor Incorporated developing software and weed detection algorithms for robotic equipment before working at Central State University. At CSU he is a computer vision specialist designing code for autonomous farming equipment. He is a US patent holder, as well as having worked on projects funded by the USDA and United States Air force.

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Torry Johnson

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Jeffrey D. Taylor Jr. Central State University

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Abstract

Deep learning structures, such as Convolutional Neural Networks (CNNs), have been introduced to the undergraduate students in a minority institution for the past three years. Funded by an 1890 Land Grant research project and a USDA Capacity Building Grant (CBG), a number of students with minimum deep learning background were trained to develop customize CNNs. After the training, the students were able to solve given plant classification problems and develop plant classification apps to showcase the performance of the customized CNNs. In particular, two students’ research projects were discussed in details in this work. One project’s goal was to identify Soybean (Glycine max) in its Cotyledon (VC) and 1st -5th trifoliate stages, the other project’s goal was to identify Hemp (Cannabis sativa) in its three variations. The databases used in these projects were built from real-life field images, which contains 9 common weed species. The students’ achievement, as well as discovered issues, are assessed and reported in this work. Some of the students’ projects will be further used to support our 1890 Land Grant and CBG research.

Cao, D., & Lowell, C., & Schluttenhofer, C. M., & Morris, A., & Erdman, A. R., & Johnson, T., & Taylor, J. D. (2020, June), Undergraduate Research: Deep Learning-based Plant Classifiers and Their Real-life Research Applications Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35411

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