Skip to main content

Simplified Version of White Wine Grape Berries Detector Based on SVM and HOG Features

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 464))

Abstract

The detection of grapes in real scene images is a serious task solved by researches dealing with precision viticulture. Our research has shown that in the case of white wine varieties, grape berry detectors based on a support vector machine classifier in combination with a HOG descriptor are very efficient. In this paper, simplified versions of our original solutions are introduced. Our research showed that skipping contrast normalization by image preprocessing accelerates the detection process; however, the performance of the detectors is not negatively influenced by this modification.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Arnó Satorra, J., Martínez Casasnovas, J.A., Ribes Dasi, M., Rosell Polo, J.R.: Review. Precision viticulture. Research topics, challenges and opportunities in site-specific vineyard management. Span. J. Agric. Res. 7(4), 779–790 (2009)

    Google Scholar 

  2. Berenstein, R., Shahar, O., Shapiro, A., Edan, Y.: Grape clusters and foliage detection algorithms for autonomous selective vineyard sprayer. Intell. Serv. Robot. 3(4), 233–243 (2010)

    Article  Google Scholar 

  3. Chamelat, R., Rosso, E., Choksuriwong, A., Rosenberger, C., Laurent, H., Bro, P.: Grape detection by image processing. In: IECON 2006—32nd Annual Conference on IEEE Industrial Electronics, pp. 3697–3702 (2006)

    Google Scholar 

  4. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 1, pp. 886–893 (2005)

    Google Scholar 

  5. Diago, M.P., Correa, C., Milln, B., Barreiro, P., Valero, C., Tardaguila, J.: Grapevine yield and leaf area estimation using supervised classification methodology on RGB images taken under field conditions. Sensors 12(12), 16988–17006 (2012)

    Article  Google Scholar 

  6. Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Pearson, 2nd edn. (2012)

    Google Scholar 

  7. ITU-R Recommendation BT.601: Studio encoding parameters of digital television for standard 4:3 and wide screen 16:9 aspect ratios (2011)

    Google Scholar 

  8. Krig, S.: Computer Vision Metrics: Survey, Taxonomy, and Analysis, 1st edn. Apress, Berkely, CA, USA (2014)

    Google Scholar 

  9. Lampert, C.H.: Kernel methods in computer vision. Found. Trends Comput. Graph. Vis. 4(3), 193–285 (2008)

    Article  MATH  Google Scholar 

  10. Liu, S., Whitty, M.: Automatic grape bunch detection in vineyards with an SVM classifier. J. Appl. Logic (2015)

    Google Scholar 

  11. Nuske, S., Achar, S., Bates, T., Narasimhan, S., Singh, S.: Yield estimation in vineyards by visual grape detection. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2352–2358. IEEE (2011)

    Google Scholar 

  12. Reis, M., Morais, R., Peres, E., Pereira, C., Contente, O., Soares, S., Valente, A., Baptista, J., Ferreira, P., Cruz, J.B.: Automatic detection of bunches of grapes in natural environment from color images. J. Appl. Logic 10(4), 285–290 (2012)

    Article  Google Scholar 

  13. Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4), 427–437 (2009)

    Article  Google Scholar 

  14. Škrabánek, P., Runarsson, T.P.: Detection of grapes in natural environment using support vector machine classifier. In: Proceedings of the 21st International Conference on Soft Computing MENDEL 2015, Brno University of Technology, Brno, Czech Republic, 23–25 Jun 2015, pp. 143–150 (2015)

    Google Scholar 

Download references

Acknowledgments

The work has been supported by the Funds of University of Pardubice, Czech Republic. We would like to offer our special thanks to company Víno Sýkora s.r.o. which enabled us to perform experiments in its vineyards.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pavel Skrabanek .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Skrabanek, P., Majerík, F. (2016). Simplified Version of White Wine Grape Berries Detector Based on SVM and HOG Features. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Silhavy, P., Prokopova, Z. (eds) Artificial Intelligence Perspectives in Intelligent Systems. Advances in Intelligent Systems and Computing, vol 464. Springer, Cham. https://doi.org/10.1007/978-3-319-33625-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-33625-1_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33623-7

  • Online ISBN: 978-3-319-33625-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics