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Review of Plant Identification Based on Image Processing

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Abstract

Plant recognition is closely related to people’s life. The operation of the traditional plant identification method is complicated, and is unfavorable for popularization. The rapid development of computer image processing and pattern recognition technology makes it possible for computer’s automatic recognition of plant species based on image processing. There are more and more researchers drawing their attention on the computer’s automatic identification technology based on plant images in recent years. Based on this, we have carried on a wide range of research and analysis on the plant identification method based on image processing in recent years. First of all, the research significance and history of plant recognition technologies are introduced in this paper; secondly, the main technologies and steps of plant recognition are reviewed; thirdly, more than 30 leaf features (including 16 shape features, 11 texture features, four color features), and then SVM was used to evaluate these features and their fusion features, and 8 commonly used classifiers are introduced in detail. Finally, the paper is ended with a conclusion of the insufficient of plant identification technologies and a prediction of future development.

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Acknowledgments

This work is jointly supported by China Postdoctoral Science Foundation (Grant No. 2013M532097), Fundamental Research Funds for the Central Universities (lzujbky-2015-197 and lzujbky-2014-52), National Science Foundation of China (Grant Nos. 61201421).

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Zhaobin Wang, Huale Li & TianFang Xu

  2. Institute of Biology, Gansu Academy of Sciences, Lanzhou, 730000, China

    Ying Zhu

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  1. Zhaobin Wang
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  2. Huale Li
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  3. Ying Zhu
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  4. TianFang Xu
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Correspondence to Zhaobin Wang.

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Wang, Z., Li, H., Zhu, Y. et al. Review of Plant Identification Based on Image Processing. Arch Computat Methods Eng 24, 637–654 (2017). https://doi.org/10.1007/s11831-016-9181-4

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