Applications of computer vision and machine learning techniques for digitized herbarium specimens: A systematic literature review |
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| Affiliation: | 1. School of Digital Science, Universiti Brunei Darussalam, Brunei Darussalam;2. Institute of Applied Data Analytics, Universiti Brunei Darussalam, Brunei Darussalam;3. Department of Environmental Life Sciences, Faculty of Science, Universiti Brunei Darussalam, Brunei Darussalam;1. Mathematics Department, Universite Libre de Bruxelles (ULB), Belgium;2. Department of N.S. and M.E. ETSNyM, University of A Coruña, Paseo de Ronda 51, 15011A Coruña, Spain;3. Ecole Polytechnique d''Architecture et d''Urbanisme (EPAU), Laboratoire Ville, Urbanisme et Développement Durable (VUDD), Route de Beaulieu, El-Harrach - BP N°177, 16200, Algiers, Algeria;1. Curso de Engenharia de Computação, Universidade Federal do Ceará - Campus de Sobral, Rua Estanislau Frota, S/N, Centro, Sobral CEP: 62010-560, Cearã, Brazil;2. Departamento de Engenharia de Teleinformática, Universidade Federal do Ceará, Campus do Pici, S/N, Bloco 725, Caixa Postal 6007, Fortaleza CEP: 60.455-970, Ceará, Brazil;3. Departamento de Ciências Biológicas, Faculdade de Ciências e Letras, Universidade Estadual Paulista, Av. Dom Antônio, 2100, Assis CEP: 19806-900, SP, Brazil;4. Faculdade de Computação, Universidade Federal de Uberlândia, Av. João Naves de Ávila, 2121, Uberlândia CEP: 38408-100, MG, Brazil |
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| 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. |
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