Applications of computer vision and machine learning techniques for digitized herbarium specimens: A systematic literature review

Abstract

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 study categorizes the different techniques and applications that are commonly used for digitized herbarium specimens and highlights existing challenges together with their potential solutions. We hope this study will serve as a firm foundation for new researchers in the relevant disciplines and will also be enlightening to both computer science and ecology experts.