Skip to main content
Springer Nature Link
Log in

RGB Images Driven Recognition of Grapevine Varieties

  • Conference paper
  • First Online:
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020) (SOCO 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1268))

Abstract

We present a grapevine variety recognition system based on a densely connected convolutional network. The proposed solution is aimed as a data processing part of an affordable sensor for selective harvesters. The system classifies size normalized RGB images according to varieties of grapes captured in the images. We train and evaluate the system on in-field images of ripe grapes captured without any artificial lighting, in a direction of sunshine likewise in the opposite direction. A dataset created for this purpose consists of 7200 images classified into 8 categories. The system distinguishes among seven grapevine varieties and background, where four and three varieties have red and green grapes, respectively. Its average per-class classification accuracy is at 98.10% and 97.47% for red and green grapes, respectively. The system also well differentiates grapes from background. Its overall average per-class accuracy is over 98%. The evaluation results show that conventional cameras in combination with the proposed system allow construction of affordable automatic selective harvesters.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Bac, C.W., Hemming, J., van Tuijl, B., Barth, R., Wais, E., van Henten, E.J.: Performance evaluation of a harvesting robot for sweet pepper. J. Field Robot. 34(6), 1123–1139 (2017). https://doi.org/10.1002/rob.21709. https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.21709

  2. Bontsema, J., Hemming, J., Pekkeriet, E., Saeys, W., Edan, Y., Shapiro, A., Hočevar, M., Oberti, R., Armada, M., Ulbrich, H., Baur, J., Debilde, B., Best, S., Evain, S., Gauchel, W., Hellström, T., Ringdahl, O.: CROPS: clever robots for crops. Eng. Technol. Ref. 1(1) (2015). https://doi.org/10.1049/etr.2015.0015

  3. Fernandes, A., Utkin, A., Eiras-Dias, J., Silvestre, J., Cunha, J., Melo-Pinto, P.: Assessment of grapevine variety discrimination using stem hyperspectral data and adaboost of random weight neural networks. Appl. Soft Comput. 72, 140–155 (2018). https://doi.org/10.1016/j.asoc.2018.07.059

    Article  Google Scholar 

  4. Galet, P.: A Practical Ampelography: Grapevine Identification, 1st edn. Comstock Pub. Associates, Ithaca (1979)

    Google Scholar 

  5. Gutiérrez, S., Tardaguila, J., Fernández-Novales, J., Diago, M.: Data mining and NIR spectroscopy in viticulture: applications for plant phenotyping under field conditions. Sensors (Switzerland) 16(2) (2016). https://doi.org/10.3390/s16020236

  6. Gutiérrez, S., Fernández-Novales, J., Diago, M.P., Tardaguila, J.: On-the-go hyperspectral imaging under field conditions and machine learning for the classification of grapevine varieties. Front. Plant Sci. 9, 1102 (2018). https://doi.org/10.3389/fpls.2018.01102

    Article  Google Scholar 

  7. Han, D., Kim, J., Kim, J.: Deep pyramidal residual networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6307–6315, July 2017. https://doi.org/10.1109/CVPR.2017.668

  8. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, June 2016. https://doi.org/10.1109/CVPR.2016.90

  9. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269, July 2017. https://doi.org/10.1109/CVPR.2017.243

  10. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Proceedings of Machine Learning Research, vol. 37, pp. 448–456. PMLR (2015)

    Google Scholar 

  11. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)

    Article  Google Scholar 

  12. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  13. Lemnaru, C., Potolea, R.: Imbalanced classification problems: systematic study, issues and best practices. In: Enterprise Information Systems, pp. 35–50. Springer, Heidelberg (2012)

    Google Scholar 

  14. Maul, et al.: Vitis international variety catalogue (2020). www.vivc.de

  15. Pelsy, F., Hocquigny, S., Moncada, X., Barbeau, G., Forget, D., Hinrichsen, P., Merdinoglu, D.: An extensive study of the genetic diversity within seven French wine grape variety collections. Theor. Appl. Genet. 120(6), 1219–1231 (2010). https://doi.org/10.1007/s00122-009-1250-8

    Article  Google Scholar 

  16. Ruder, S.: An overview of gradient descent optimization algorithms. CoRR abs/1609.04747 (2016)

    Google Scholar 

  17. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 60 (2019). https://doi.org/10.1186/s40537-019-0197-0

    Article  Google Scholar 

  18. Slaughter, D., Giles, D., Downey, D.: Autonomous robotic weed control systems: a review. Comput. Electron. Agric. 61(1), 63–78 (2008). https://doi.org/10.1016/j.compag.2007.05.008

    Article  Google Scholar 

  19. Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4), 427–437 (2009). https://doi.org/10.1016/j.ipm.2009.03.002

    Article  Google Scholar 

  20. Soldavini, C., Schneider, A., Stefanini, M., Dallaserra, M., Policarpo, M.: Super ampelo, a software for ampelometric and ampelographic descriptions in vitis. Acta Horticulturae 827, 253–258 (2009). https://doi.org/10.17660/ActaHortic.2009.827.43

    Article  Google Scholar 

  21. de Soto, M.G., Emmi, L., Perez-Ruiz, M., Aguera, J., de Santos, P.G.: Autonomous systems for precise spraying - evaluation of a robotised patch sprayer. Biosyst. Eng. 146, 165–182 (2016). https://doi.org/10.1016/j.biosystemseng.2015.12.018

    Article  Google Scholar 

  22. Srivastava, R.K., Greff, K., Schmidhuber, J.: Training very deep networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2, NIPS 2015, pp. 2377–2385. MIT Press, Cambridge (2015)

    Google Scholar 

  23. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826, June 2016. https://doi.org/10.1109/CVPR.2016.308

  24. Xiong, Y., Peng, C., Grimstad, L., From, P.J., Isler, V.: Development and field evaluation of a strawberry harvesting robot with a cable-driven gripper. Comput. Electron. Agr. 157, 392–402 (2019). https://doi.org/10.1016/j.compag.2019.01.009. http://www.sciencedirect.com/science/article/pii/S0168169918312456

  25. Xu, Y., Jia, Z., Ai, Y., Zhang, F., Lai, M., Chang, E.I.: Deep convolutional activation features for large scale brain tumor histopathology image classification and segmentation. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 947–951, April 2015. https://doi.org/10.1109/ICASSP.2015.7178109

  26. Yu, Z., Li, T., Luo, G., Fujita, H., Yu, N., Pan, Y.: Convolutional networks with cross-layer neurons for image recognition. Inf. Sci. 433–434, 241–254 (2018). https://doi.org/10.1016/j.ins.2017.12.045

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

The work was supported from ERDF/ESF “Cooperation in Applied Research between the University of Pardubice and companies, in the Field of Positioning, Detection and Simulation Technology for Transport Systems (PosiTrans)” (No. CZ.02.1.01/0.0/0.0/17_049/0008394).

Author information

Authors and Affiliations

  1. Brno University of Technology, Brno, Czech Republic

    Pavel Škrabánek, Radomil Matoušek & Petr Junek

  2. University of Pardubice, Pardubice, Czech Republic

    Petr Doležel

Authors
  1. Pavel Škrabánek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Petr Doležel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Radomil Matoušek
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Petr Junek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pavel Škrabánek .

Editor information

Editors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Álvaro Herrero

  2. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Carlos Cambra

  3. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Daniel Urda

  4. Technological Institute of Castilla y León, Burgos, Spain

    Javier Sedano

  5. Department of Industrial Engineering, University of A Coruña, La Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

Rights and permissions

Reprints and permissions

Copyright information

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

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Škrabánek, P., Doležel, P., Matoušek, R., Junek, P. (2021). RGB Images Driven Recognition of Grapevine Varieties. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_21

Download citation

Publish with us

Policies and ethics