Application of Computer Vision and Machine Learning for Digitized Herbarium Specimens: A Systematic Literature Review
It provides a foundational guide for beginners and experts in computer science and ecology to leverage digitized herbarium data, though it is incremental as a review.
This systematic literature review analyzed over 50 studies applying computer vision and machine learning to digitized herbarium specimens, categorizing techniques and highlighting challenges and solutions to accelerate scientific discoveries in ecology.
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.