Multimedia Tools and Applications

, Volume 78, Issue 3, pp 3613–3632 | Cite as

Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation

  • Yu-Dong ZhangEmail author
  • Zhengchao Dong
  • Xianqing Chen
  • Wenjuan Jia
  • Sidan Du
  • Khan MuhammadEmail author
  • Shui-Hua WangEmail author


Fruit category identification is important in factories, supermarkets, and other fields. Current computer vision systems used handcrafted features, and did not get good results. In this study, our team designed a 13-layer convolutional neural network (CNN). Three types of data augmentation method was used: image rotation, Gamma correction, and noise injection. We also compared max pooling with average pooling. The stochastic gradient descent with momentum was used to train the CNN with minibatch size of 128. The overall accuracy of our method is 94.94%, at least 5 percentage points higher than state-of-the-art approaches. We validated this 13-layer is the optimal structure. The GPU can achieve a 177× acceleration on training data, and a 175× acceleration on test data. We observed using data augmentation can increase the overall accuracy. Our method is effective in image-based fruit classification.


Convolutional neural network Fully connected layer Softmax Fruit category identification 



This study was supported by Natural Science Foundation of China (61602250), Natural Science Foundation of Jiangsu Province (BK20150983), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01).

Compliance with ethical standards

Conflict of interest

We have no conflicts of interest to disclose with regard to the subject matter of this paper.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHenan Polytechnic UniversityJiaozuoPeople’s Republic of China
  2. 2.Jiangsu Key Laboratory of Advanced Manufacturing TechnologyHuaiyinChina
  3. 3.Translational Imaging Division & MRI UnitColumbia University and New York State Psychiatric InstituteNew YorkUSA
  4. 4.Department of electrical engineering, College of engineeringZhejiang Normal UniversityZhejiangChina
  5. 5.School of Computer Science and TechnologyNanjing Normal UniversityNanjingChina
  6. 6.School of Electronic Science and EngineeringNanjing UniversityNanjingChina
  7. 7.College of Software ConvergenceSejong UniversitySeoulRepublic of Korea

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