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, 25 (2), 507-543

Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review

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Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review

Jana Wäldchen et al. Arch Comput Methods Eng.

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 and concise 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.

Conflict of interest statement

Compliance with ethical standardsThe authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Generic steps of an image-based plant classification process (green-shaded boxes are the main focus of this review). (Color figure online)
Fig. 2
Fig. 2
Study selection process
Fig. 3
Fig. 3
Number of studies per year of publication
Fig. 4
Fig. 4
Leaf structure, leaf types, and flower structure
Fig. 5
Fig. 5
Distribution of the maximum evaluated species number per study. Six studies [76, 100, 101, 107, 108, 112] provide no information about the number of studied species. If more than one dataset per paper was used, species numbers refer to the largest dataset evaluated
Fig. 6
Fig. 6
Distribution of the maximum evaluated images number per study. Six studies [10, 53, 76, 118, 132, 135] provide no information about the number of used images. If more than one dataset per paper was used, image numbers refer to the largest dataset evaluated
Fig. 7
Fig. 7
Categorization (green shaded boxes) and overview (green framed boxes) of the most prominent feature descriptors in plant species identification. Feature descriptors partly fall in multiple categories. (Color figure online)

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