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  Plant species identification using computer vision: A systematic literature review

Wäldchen, J., & Mäder, P. (2018). Plant species identification using computer vision: A systematic literature review. Archives of computational methods in engineering, 25(2), 507-543. doi:10.1007/s11831-016-9206-z.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002C-3D93-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0000-F96B-0
Genre: Journal Article

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http://dx.doi.org/10.1007/s11831-016-9206-z (Publisher version)
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Wäldchen, Jana1, Author              
Mäder, Patrick, Author
Affiliations:
1Department Biogeochemical Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_1688139              

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 Abstract: Species knowledge is essential for protecting biodiversity. The identification of plants by conventional keys is complex, time consuming, and due to the use of specific botanical terms frustrating for non-experts. This creates a hard to overcome hurdle for novices interested in acquiring species knowledge. Today, there is an increasing interest in automating the process of species identification. The availability and ubiquity of relevant technologies, such as, digital cameras and mobile devices, the remote access to databases, new techniques in image processing and pattern recognition let the idea of automated species identification become reality. This paper is the first systematic literature review with the aim of a thorough analysis and comparison of primary studies on computer vision approaches for plant species identification. We identified 120 peer-reviewed studies, selected through a multi-stage process, published in the last 10 years (2005–2015). After a careful analysis of these studies, we describe the applied methods categorized according to the studied plant organ, and the studied features, i.e., shape, texture, color, margin, and vein structure. Furthermore, we compare methods based on classification accuracy achieved on publicly available datasets. Our results are relevant to researches in ecology as well as computer vision for their ongoing research. The systematic andconcise overview will also be helpful for beginners in those research fields, as they can use the comparable analyses of applied methods as a guide in this complex activity

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 Dates: 2016-11-242017-01-072018-04
 Publication Status: Published in print
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 Identifiers: Other: BGC2577
DOI: 10.1007/s11831-016-9206-z
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Title: Archives of computational methods in engineering
  Abbreviation : Arch Computat Methods Eng
Source Genre: Journal
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Publ. Info: Barcelona : CIMNE : Springer
Pages: - Volume / Issue: 25 (2) Sequence Number: - Start / End Page: 507 - 543 Identifier: ISSN: 1134-3060
CoNE: /journals/resource/1134-3060