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Real-Time Plant Species Recognition Using Non-averaged DenseNet-169 Deep Learning Paradigm

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Computer Vision and Image Processing (CVIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1777))

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Abstract

Real-time plant species recognition under unconstrained environment (viewpoint variation, changing background, scale variation, illumination changes etc.) is a challenging and time-consuming process. In this paper, a non-averaged DenseNet-169 (NADenseNet-169) CNN architecture is proposed and demonstrated to perform real-time plant species recognition. The architecture is evaluated on two datasets namely, Flavia (Standard) and Leaf-12 (custom created). The hyperparameters (optimizers, learning rate) are optimized to achieve higher performance metrics with lower computation time. From the experimental investigation, it is observed that Adam optimizer with a learning rate of 0.0001 (Batch size of 32) resulted in obtaining higher performance metrics. In case of Flavia dataset, an accuracy of 98.58% is obtained with a computational time of 3.53 s. For Leaf-12 dataset, an accuracy of 99% is obtained with a computational time of 4.45 s. The model trained on Leaf-12 dataset performed better in identifying the plant species under unconstrained environment.

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Acknowledgements

The authors would like to thank NVIDIA for providing Titan X GPU under the University Research Grant Programme.

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Authors and Affiliations

  1. Department of Electronics Engineering, MIT Campus, Anna University, Chennai, India

    V. Sathiesh Kumar

  2. Department of Computer Science Engineering, SRM Institute of Science and Technology, Chennai, India

    S. Anubha Pearline

Authors
  1. V. Sathiesh Kumar
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  2. S. Anubha Pearline
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Corresponding author

Correspondence to V. Sathiesh Kumar .

Editor information

Editors and Affiliations

  1. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Deep Gupta

  2. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Kishor Bhurchandi

  3. Indian Institute of Technology Ropar, Rupnagar, India

    Subrahmanyam Murala

  4. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  5. Indian Institute of Technology Roorkee, Roorkee, India

    Sanjeev Kumar

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Sathiesh Kumar, V., Anubha Pearline, S. (2023). Real-Time Plant Species Recognition Using Non-averaged DenseNet-169 Deep Learning Paradigm. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_5

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  • DOI: https://doi.org/10.1007/978-3-031-31417-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31416-2

  • Online ISBN: 978-3-031-31417-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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