Proof of Concept: Autonomous Machine Vision Software for Botanical Identification

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Authors

Jonathan P. Leidig · Grand Valley State University

Gregory S. Wolffe · Grand Valley State University

Abstract

This use of an autonomous machine-vision system for botanical identification removed the subjectivity inherent to human-based evaluation. The learned model also accurately evaluated botanical HPTLC images significantly faster than its human counterpart, which could save both time and resources.

Topics & Keywords

Traditional Chinese Medicine Analysis Spectroscopy and Chemometric Analyses Traditional Chinese Medicine Studies

Publication Details

Published in: Journal of AOAC International

Volume 109, Issue 2, pp. 193-199

DOI: 10.1093/jaoacint/qsae091

Field-Weighted Citation Impact: 2.87

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Proof of Concept: Autonomous Machine Vision Software for Botanical Identification

Authors

Abstract

Topics & Keywords

Publication Details