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

By Jana Wäldchen, Patrick Mäder. Archives of Computational Methods in Engineering, 2017

425 citations recorded by the source corpus.

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.

Subfields: Ecological Modeling, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/b323aa63247c6a1f3076bee55e49188a09d6c6ab

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Citations in the corpus

Machine learning for image based species identification (2018, background): “…based species identification tasks are manifold and were comprehensively surveyed before, e.g. by Cope, Corney, Clark, Remagnino, and Wilkin (2012); Moniruzzaman, Islam, Bennamoun, and Lavery (2017); Seeland, Rzanny, Alaqraa, Wäldchen, and Mäder (2017); Wäldchen and Mäder (2018); Weinstein (2018).”
Automated plant species identification—Trends and future directions (2018, methodology): “Extensive overviews of the utilized characteristics, as well as of the methods used for capturing them in a formal description, are given by Wäldchen and Mäder [16] and Cope et al. [17].”
The Herbarium Challenge 2019 Dataset (2019, background): “The use of Artificial Intelligence for automatic species identification is a new and rapidly developing field and holds promise for identifying the still to be named species in herbaria [4, 5, 11, 15, 17, 18, 19, 20, 22].”
Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective (2018, neutral): “Wäldchen and Mäder have thoroughly summarized the literature on computer vision-based species identification published by 2016 [54].”