Skip to main content
Book cover

International Conference on Intelligent Systems Design and Applications

ISDA 2020: Intelligent Systems Design and Applications pp 181–189Cite as

Employment of Pre-trained Deep Learning Models for Date Classification: A Comparative Study

  • 1220 Accesses

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1351)

Abstract

Classification of dates in an orchard environment is a challenging task due to various texture, color, shape, and size properties. Moreover, the date has various data types that have almost the same appearance and makes classification much more difficult. To overcome these limitations, deep learning offers effective models that automatically extract features better than traditional machine learning techniques. Although deep learning models have shown excellent performance in several tasks, they require a large amount of training data to perform well. To address this issue, and to attain effective models to classify dates in an orchard environment, we employed pre-trained deep learning models. These models have been trained with a large amount of data and they showed outstanding performance in image classification. We have fine-tuned four pre-trained models; GoogleNet, ResNet-50, DenseNet and AlexNet for classifying date types. Our experimental results show that ResNet-50 has achieved the highest F1-score (98.14%) and accuracy (97.37%), compared to other models and previous methods that worked on the same dataset.

Keywords

  • Date classification
  • Deep learning
  • Pre-trained models

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-71187-0_17
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   219.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-71187-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   279.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.

References

  1. Food and Agriculture Organization Corporate Statistical. FAO-STAT (2016). Accessed 18 Oct 2018, https://www.fao.org/faostat/en/#data/QC

  2. Al-Janobi, A., Abdulwahed, A.: Evaluation of field test of harvesting system for picking dates fruits based on robotic arm. In: Proceedings of the First International Conference on Robotics and Associated High-technologies and Equipment for Agriculture. Applications of Automated Systems and Robotics for Crop Protection in Sustainable Precision Agriculture (RHEA-2012), Pisa, Italy, 19–21 September 2012, pp. 183–188. University of Pisa (2012)

    Google Scholar 

  3. Bac, C.W., van Henten, E.J., Hemming, J., Edan, Y.: Harvesting robots for high-value crops: State-of-the-art review and challenges ahead. J. Field Rob. 31(6), 888–911 (2014)

    CrossRef  Google Scholar 

  4. Arefi, A., Motlagh, A.M., Mollazade, K., Teimourlou, R.F.: Recognition and localization of ripen tomato based on machine vision. Aust. J. Crop Sci. 5(10), 1144 (2011)

    Google Scholar 

  5. Alzubaidi, L., Al-Shamma, O., Fadhel, M.A., Arkah, Z.M., Awad, F.H.: A deep convolutional neural network model for multi-class fruits classification. In: International Conference on Intelligent Systems Design and Applications, pp. 90–99. Springer, Cham (2019)

    Google Scholar 

  6. Hobani, A.I., Thottam, A.M., Ahmed, K.A.: Development of a neural network classifier for date fruit varieties using some physical attributes. King Saud University-Agricultural Research Center (2003)

    Google Scholar 

  7. Fadel, M.: Date fruits classification using probabilistic neural networks. Agric. Eng. Int. CIGR J. IX, 1–11 (2007)

    Google Scholar 

  8. Haidar, A., Dong, H., Mavridis, N.: Image-based date fruit classification. In: 2012 IV International Congress on Ultra Modern Telecommunications and Control Systems, pp. 357–363. IEEE (2012)

    Google Scholar 

  9. Muhammad, G.: Date fruits classification using texture descriptors and shape-size features. Eng. Appl. Artif. Intell. 37, 361–367 (2015)

    CrossRef  Google Scholar 

  10. Aiadi, O., Kherfi, M.L.: A new method for automatic date fruit classification. Int. J. Comput. Vision Rob. 7(6), 692–711 (2017)

    CrossRef  Google Scholar 

  11. Hossain, M.S., Muhammad, G., Amin, S.U.: Improving consumer satisfaction in smart cities using edge computing and caching: a case study of date fruits classification. Fut. Gener. Comput. Syst. 88, 333–341 (2018)

    CrossRef  Google Scholar 

  12. Alzubaidi, L., Al-Amidie, M., Al-Asadi, A., Humaidi, A.J., Al-Shamma, O., Fadhel, M.A., Zhang, J., Santamaría, J., Duan, Y.: Novel transfer learning approach for medical imaging with limited labeled data. Cancers 13, 1590 (2021). https://doi.org/10.3390/cancers13071590

  13. Alzubaidi, L., Al-Shamma, O., Fadhel, M.A., Farhan, L., Zhang, J., Duan, Y.: Optimizing the performance of breast cancer classification by employing the same domain transfer learning from hybrid deep convolutional neural network model. Electronics 9(3), 445 (2020)

    CrossRef  Google Scholar 

  14. Hasan, R.I., Yusuf, S.M., Alzubaidi, L.: Review of the state of the art of deep learning for plant diseases: a broad analysis and discussion. Plants 9(10), 1302 (2020)

    CrossRef  Google Scholar 

  15. Alzubaidi, L., Fadhel, M.A., Al-Shamma, O., Zhang, J., Santamaría, J., Duan, Y., Oleiwi, S.R.: Towards a better understanding of transfer learning for medical imaging: a case study. Appl. Sci. 10(13), 4523 (2020)

    CrossRef  Google Scholar 

  16. Al-Shamma, O., Fadhel, M.A., Hameed, R.A., Alzubaidi, L., Zhang, J.: Boosting convolutional neural networks performance based on FPGA accelerator. In: International Conference on Intelligent Systems Design and Applications, pp. 509–517. Springer, Cham (2018)

    Google Scholar 

  17. Fadhel, M.A., Al-Shamma, O., Oleiwi, S.R., Taher, B.H., Alzubaidi, L.: Real-time PCG diagnosis using FPGA. In: International Conference on Intelligent Systems Design and Applications, pp. 518–529. Springer, Cham (2018)

    Google Scholar 

  18. Altaheri, H., Alsulaiman, M., Muhammad, G.: Date fruit classification for robotic harvesting in a natural environment using deep learning. IEEE Access 7, 117115–117133 (2019)

    CrossRef  Google Scholar 

  19. Alzubaidi, L., Zhang, J., Humaidi, A.J., et al.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8, 53 (2021). https://doi.org/10.1186/s40537-021-00444-8

  20. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  21. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: European Conference on Computer Vision, pp. 818–833. Springer, Cham (2014)

    Google Scholar 

  22. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  23. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  24. Karpathy, A., Johnson, J., Fei-Fei, L.: Visualizing and understanding recurrent networks (2015). arXiv preprint arXiv:1506.02078

  25. Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks (2015). arXiv preprint arXiv:1505.00387

  26. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  27. Targ, S., Almeida, D., Lyman, K.: Resnet in resnet: Generalizing residual architectures (2016). arXiv preprint arXiv:1603.08029

  28. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Al-Nahrain University, Baghdad, Iraq

    Aiman Al-Sabaawi

  2. University of Information Technology and Communications, Baghdad, Iraq

    Reem Ibrahim Hasan, Omran Al-Shamma & Laith Alzubaidi

  3. College of Computer Science and Information Technology, University of Sumer, Rifai, ThiQar, Iraq

    Mohammed A. Fadhel

  4. Faculty of Science and Engineering, Queensland University of Technology, Brisbane, Australia

    Laith Alzubaidi

Authors
  1. Aiman Al-Sabaawi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reem Ibrahim Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammed A. Fadhel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Omran Al-Shamma
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Laith Alzubaidi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Laith Alzubaidi .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Prof. Dr. Ajith Abraham

  2. Department of Computer Science, Università degli Studi di Milano, Milan, Milano, Italy

    Prof. Vincenzo Piuri

  3. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Dr. Niketa Gandhi

  4. Campus Centre de Créteil, Université Paris-Est Créteil, Créteil, France

    Prof. Patrick Siarry

  5. Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Prof. Dr. Dr. h. c. Arturas Kaklauskas

  6. School of Engineering, Instituto Superior de Engenharia do Porto, Porto, Portugal

    Dr. Ana Madureira

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Al-Sabaawi, A., Hasan, R.I., Fadhel, M.A., Al-Shamma, O., Alzubaidi, L. (2021). Employment of Pre-trained Deep Learning Models for Date Classification: A Comparative Study. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_17

Download citation