Performance comparison of different convolutional neural networks for vegetable and fruit recognition

Research Article
Open access

Performance comparison of different convolutional neural networks for vegetable and fruit recognition

Yuxuan Chen 1 , Shengkai Pan 2 , Haoyu Wang 3*
  • 1 Sun Yat-sen University, Guangzhou, 510275, China    
  • 2 Wuhan University of Technology, Wuhan, 430070, China    
  • 3 The University of New South Wales, Sydney, 2052, Australia    
  • *corresponding author z5242863@unsw.edu.au
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

Image classification, a significant research problem in the computer vision community, aims to assign different types of images to a certain category in a fixed category set according to different information features reflected in images. With the continuous improvement of living standards, people's daily demand for accurate identification of food categories (such as vegetable, fruit, etc.) is growing. Early vegetable and fruit recognition mostly relied on manual features and machine learning algorithms, with low recognition accuracy and weak generalization ability. Thanks to the rapid evolution of the convolutional neural network, vegetable and fruit recognition based on depth learning has made breakthroughs in accuracy and speed. In this paper, three representative convolutional neural networks are introduced around vegetable and fruit recognition, and their performance differences are quantitatively compared on different data sets to explore the boundaries of their applications. In addition, we summarized the research problems in vegetable and fruit recognition and discussed its future development direction.

Keywords:

image classification, deep learning, computer vision, vegetable and fruit.

Chen,Y.;Pan,S.;Wang,H. (2023). Performance comparison of different convolutional neural networks for vegetable and fruit recognition. Applied and Computational Engineering,5,593-602.
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References

[1]. LeCun, Y., Bengio, Y. and Hinton, G., 2015. Deep learning. nature, 521(7553), pp.436-444.

[2]. Pan, S.J. and Yang, Q., 2009. A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10), pp.1345-1359.

[3]. Krizhevsky, A., Sutskever, I. and Hinton, G.E., 2017. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), pp.84-90.

[4]. Simonyan, K. and Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[5]. He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

[6]. Tan, M. and Le, Q., 2019, May. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning (pp. 6105-6114). PMLR.

[7]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I., 2017. Attention is all you need. Advances in neural information processing systems, 30.

[8]. Hou, S., Feng, Y. and Wang, Z., 2017. Vegfru: A domain-specific dataset for fine-grained visual categorization. In Proceedings of the IEEE International Conference on Computer Vision (pp. 541-549).

[9]. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A., 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition(pp. 1-9).

[10]. Huang, G., Liu, Z., Van Der Maaten, L. and Weinberger, K.Q., 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).

[11]. Ronneberger, O., Fischer, P. and Brox, T., 2015, October. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.

Cite this article

Chen,Y.;Pan,S.;Wang,H. (2023). Performance comparison of different convolutional neural networks for vegetable and fruit recognition. Applied and Computational Engineering,5,593-602.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. LeCun, Y., Bengio, Y. and Hinton, G., 2015. Deep learning. nature, 521(7553), pp.436-444.

[2]. Pan, S.J. and Yang, Q., 2009. A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10), pp.1345-1359.

[3]. Krizhevsky, A., Sutskever, I. and Hinton, G.E., 2017. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), pp.84-90.

[4]. Simonyan, K. and Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

[5]. He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

[6]. Tan, M. and Le, Q., 2019, May. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning (pp. 6105-6114). PMLR.

[7]. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I., 2017. Attention is all you need. Advances in neural information processing systems, 30.

[8]. Hou, S., Feng, Y. and Wang, Z., 2017. Vegfru: A domain-specific dataset for fine-grained visual categorization. In Proceedings of the IEEE International Conference on Computer Vision (pp. 541-549).

[9]. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A., 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition(pp. 1-9).

[10]. Huang, G., Liu, Z., Van Der Maaten, L. and Weinberger, K.Q., 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708).

[11]. Ronneberger, O., Fischer, P. and Brox, T., 2015, October. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-241). Springer, Cham.

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