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Plant identification based on very deep convolutional neural networks

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Abstract

Plant identification is a critical step in protecting plant diversity. However, many existing identification systems prohibitively rely on hand-crafted features for plant species identification. In this paper, a deep learning method is employed to extract discriminative features from plant images along with a linear SVM for plant identification. To offer a self-learning feature representation for different plant organs, we choose a very deep convolutional neural networks (CNNs), which consists of sixteen convolutional layers followed by three Fully-Connected (FC) layers and a final soft-max layer. Five max-pooling layers are performed over a 2×2 pixel window with stride 2. Extensive experiments on several plant datasets demonstrate the remarkable performance of the very deep neural network compared to the hand-crafted features.

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Acknowledgments

This work is supported by the key R&D program of Yantai City (No. 2016YT06000609).

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Authors and Affiliations

  1. School of Information, Beijing Forestry University, Beijing, China

    Heyan Zhu

  2. School of Opto-Electronic Information, Yantai University, Yantai, China

    Heyan Zhu

  3. School of Computer Science and Technology, Harbin Institute of Technology, Weihai, China

    Qinglin Liu & Shengping Zhang

  4. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Yuankai Qi & Feng Jiang

  5. Institute of Animation and digital art, Communication University of China, Beijing, China

    Xinyuan Huang

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  1. Heyan Zhu
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  2. Qinglin Liu
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  3. Yuankai Qi
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  4. Xinyuan Huang
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  5. Feng Jiang
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  6. Shengping Zhang
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Correspondence to Shengping Zhang.

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Zhu, H., Liu, Q., Qi, Y. et al. Plant identification based on very deep convolutional neural networks. Multimed Tools Appl 77, 29779–29797 (2018). https://doi.org/10.1007/s11042-017-5578-9

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