Zhang, Zichen, Khanal, Sami, Raudenbush, Amy, Tilmon, Kelley, and Stewart, Christopher. Assessing the efficacy of machine learning techniques to characterize soybean defoliation from unmanned aerial vehicles. Retrieved from https://par.nsf.gov/biblio/10318214. Computers and Electronics in Agriculture 193.C Web. doi:10.1016/j.compag.2021.106682.

Zhang, Zichen, Khanal, Sami, Raudenbush, Amy, Tilmon, Kelley, & Stewart, Christopher. Assessing the efficacy of machine learning techniques to characterize soybean defoliation from unmanned aerial vehicles. Computers and Electronics in Agriculture, 193 (C). Retrieved from https://par.nsf.gov/biblio/10318214. https://doi.org/10.1016/j.compag.2021.106682

Zhang, Zichen, Khanal, Sami, Raudenbush, Amy, Tilmon, Kelley, and Stewart, Christopher. "Assessing the efficacy of machine learning techniques to characterize soybean defoliation from unmanned aerial vehicles". Computers and Electronics in Agriculture 193 (C). Country unknown/Code not available. https://doi.org/10.1016/j.compag.2021.106682. https://par.nsf.gov/biblio/10318214.

@article{osti_10318214,
place = {Country unknown/Code not available}, title = {Assessing the efficacy of machine learning techniques to characterize soybean defoliation from unmanned aerial vehicles}, url = {https://par.nsf.gov/biblio/10318214}, DOI = {10.1016/j.compag.2021.106682}, abstractNote = {}, journal = {Computers and Electronics in Agriculture}, volume = {193}, number = {C}, author = {Zhang, Zichen and Khanal, Sami and Raudenbush, Amy and Tilmon, Kelley and Stewart, Christopher}, }