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Fruit category classification via an eight-layer convolutional neural network with parametric rectified linear unit and dropout technique

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Abstract

In this paper, we apply an improved deep convolutional neural network (CNN) in fruit category classification, which is a hotspot in computer vision field. We created an 8-layer deep convolutional neural network, and utilized parametric rectified linear unit to take the place of plain rectified linear unit, and placed dropout layer before each fully-connected layer. Data augmentation was used to help avoid overfitting. Our 8-layer deep convolutional neural network secured an overall accuracy of 95.67%. This proposed 8-layer method performs better than five state-of-the-art methods using traditional machine learning methods and one state-of-the-art CNN method.

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Acknowledgements

This paper was funded by Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology (HGAMTL-1703), Henan Key Research and Development Project (182102310629), National key research and development plan (2017YFB1103202), Natural Science Foundation of China (61602250).

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Authors and Affiliations

  1. Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China

    Shui-Hua Wang

  2. Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Shui-Hua Wang

  3. School of Architecture Building and Civil engineering, Loughborough University, Loughborough, LE11 3TU, UK

    Shui-Hua Wang

  4. School of Computer Science and Technology, Nanjing Normal University, Nanjing, 210008, Jiangsu, China

    Yi Chen

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  1. Shui-Hua Wang
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  2. Yi Chen
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Correspondence to Shui-Hua Wang or Yi Chen.

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Wang, SH., Chen, Y. Fruit category classification via an eight-layer convolutional neural network with parametric rectified linear unit and dropout technique. Multimed Tools Appl 79, 15117–15133 (2020). https://doi.org/10.1007/s11042-018-6661-6

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  • DOI: https://doi.org/10.1007/s11042-018-6661-6

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