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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
