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Leaf recognition based on PCNN

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Abstract

Plant is closely related to humans. How to quickly recognize an unknown plant without related professional knowledge is a huge challenge. With the development of image processing and pattern recognition, it is available for plant recognition based on the technique of image processing. Pulse-coupled neural network is a powerful tool for image processing. It is widely applied in the field of image segmentation, image fusion, feature extraction, etc. Support vector machine is an excellent classifier, which can finish the complex task of data exploration. Based on these two techniques, a novel plant recognition method is proposed in this paper. The key feature is the entropy sequence obtained by pulse-coupled neural network. Other ancillary features can be computed directly by mathematical and morphological methods. Both key feature and ancillary features are employed to represent the unique feature of one plant. Support vector machine in our method is taken as the classifier, which can implement the multi-class classification. Experimental results show that the proposed method can finish the task of plant recognition effectively. Compared with the existing methods, our proposed method has better recognition rate.

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Acknowledgments

The authors thank all the reviewers for their valuable comments, which further improved the quality of the paper. This work is jointly supported by China Postdoctoral Science Foundation (Grant No. 2013M532097), Fundamental Research Funds for the Central Universities (lzujbky-2014-52), National Science Foundation of China (Grant Nos. 61201421, 61175012 & 91125005/D011004), Science Foundation of Gansu Province of China (Grant No. 1208RJYA058) and the Incubation Foundation for Special Disciplines of the National Science Foundation of China (Grant No. J1210003/J0109).

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Authors and Affiliations

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

    Zhaobin Wang, Xiaoguang Sun & Yide Ma

  2. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, 730000, Gansu, China

    Zhaobin Wang & Yaonan Zhang

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

    Zhu Ying

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  1. Zhaobin Wang
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  2. Xiaoguang Sun
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  3. Yaonan Zhang
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  4. Zhu Ying
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  5. Yide Ma
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Correspondence to Yaonan Zhang.

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Wang, Z., Sun, X., Zhang, Y. et al. Leaf recognition based on PCNN. Neural Comput & Applic 27, 899–908 (2016). https://doi.org/10.1007/s00521-015-1904-1

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  • DOI: https://doi.org/10.1007/s00521-015-1904-1

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