Virtual On line
June 22, 2020
June 22, 2020
June 26, 2021
Electrical and Computer
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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