  Herbarium contains treasures of millions of specimens which have been
preserved for several years for scientific studies. To speed up more scientific
discoveries, a digitization of these specimens is currently on going to
facilitate easy access and sharing of its data to a wider scientific community.
Online digital repositories such as IDigBio and GBIF have already accumulated
millions of specimen images yet to be explored. This presents a perfect time to
automate and speed up more novel discoveries using machine learning and
computer vision. In this study, a thorough analysis and comparison of more than
50 peer-reviewed studies which focus on application of computer vision and
machine learning techniques to digitized herbarium specimen have been examined.
The study categorizes different techniques and applications which have been
commonly used and it also highlights existing challenges together with their
possible solutions. It is our hope that the outcome of this study will serve as
a strong foundation for beginners of the relevant field and will also shed more
light for both computer science and ecology experts.
