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Herbaria contain the treasure of millions of specimens that have been preserved for several years for scientific studies. To increase the rate of scientific discoveries, digitization of these specimens is currently ongoing to facilitate the easy access and sharing of data to a wider scientific community. Online digital repositories such as Integrated Digitized Biocollection and the Global Biodiversity Information Facility have already accumulated millions of specimen images yet to be explored. This presents the perfect time to take advantage of the opportunity to automate the identification process and increase the rate of novel discoveries using computer vision (CV) and machine learning (ML) techniques. In this study, a systematic literature review of more than 70 peer-reviewed publications was conducted focusing on the application of computer vision and machine learning techniques to digitized herbarium specimens. The …