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Grapes Visual Segmentation for Harvesting Robots Using Local Texture Descriptors

  • Eftichia Badeka
  • Theofanis Kalabokas
  • Konstantinos Tziridis
  • Alexander Nicolaou
  • Eleni Vrochidou
  • Efthimia Mavridou
  • George A. PapakostasEmail author
  • Theodore Pachidis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11754)

Abstract

This paper investigates the performance of Local Binary Patterns variants in grape segmentation for autonomous agricultural robots, namely Agrobots, applied to viniculture and winery. Robust fruit detection is challenging and needs to be accurate to enable the Agrobot to execute demanding tasks of precise farming. Segmentation task is handled by classification with the supervised machine learning model k-Nearest Neighbor (\( k \)-NN), including extracted features from Local Binary Patterns (LBP) and their variants in combination of color components. LBP variants are tested for both varieties of red and white grapes, subject to performance measures of accuracy, recall and precision. The results for red grapes indicate an approximate intended accuracy of 94% of detection, while the results relating to white grapes confirm the concerns of complex indiscreet visual cues providing accuracies of 83%.

Keywords

Visual computing Computer vision Grapes detection Image segmentation Local binary patterns 

Notes

Acknowledgment

This research has been co‐financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T1EDK-00300).

References

  1. 1.
    Rosenberger, C., Arguenon, V., Bergues-Lagarde, A., Bro, P., Rosenberger, C., Smari, W.: Multi-agent based prototyping of agriculture robots using additional soft biometric features to enhance the keystroke dynamics biometric system view project (2006)Google Scholar
  2. 2.
    Ceres, R., Pons, J.L., Jiménez, A.R., Martín, J.M., Calderón, L.: Design and implementation of an aided fruit-harvesting robot (Agribot). Ind. Robot Int. J. 25, 337–346 (1998)CrossRefGoogle Scholar
  3. 3.
    Luo, L., Tang, Y., Zou, X., Ye, M., Feng, W., Li, G.: Vision-based extraction of spatial information in grape clusters for harvesting robots. Biosys. Eng. 151, 90–104 (2016)CrossRefGoogle Scholar
  4. 4.
    Luo, L., Tang, Y., Zou, X., Wang, C., Zhang, P., Feng, W.: Robust grape cluster detection in a vineyard by combining the AdaBoost framework and multiple color components. Sensors 16, 2098 (2016)CrossRefGoogle Scholar
  5. 5.
    Chamelat, R., Rosso, E., Choksuriwong, A., Rosenberger, C., Laurent, H., Bro, P.: Grape detection by image processing. In: IECON Proceedings of Industrial Electronics Conference, pp. 3697–3702 (2006)Google Scholar
  6. 6.
    Aquino, A., Millan, B., Gutiérrez, S., Tardáguila, J.: Grapevine flower estimation by applying artificial vision techniques on images with uncontrolled scene and multi-model analysis. Comput. Electron. Agric. 119, 92–104 (2015)CrossRefGoogle Scholar
  7. 7.
    Font, D., Tresanchez, M., Martínez, D., Moreno, J., Clotet, E., Palacín, J.: Vineyard yield estimation based on the analysis of high resolution images obtained with artificial illumination at night. Sensors (Switzerland) 15, 8284–8301 (2015)CrossRefGoogle Scholar
  8. 8.
    Wang, C., Lee, W.S., Zou, X., Choi, D., Gan, H.: Detection and counting of immature green citrus fruit based on the local binary patterns (LBP) feature using illumination - normalized images. Precis. Agric. 19, 1062–1083 (2018)CrossRefGoogle Scholar
  9. 9.
    Pérez-zavala, R., Torres-torriti, M., Cheein, F.A., Troni, G.: Original papers a pattern recognition strategy for visual grape bunch detection in vineyards. Comput. Electron. Agric. 151, 136–149 (2018)CrossRefGoogle Scholar
  10. 10.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection (2016)Google Scholar
  11. 11.
    He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2017)Google Scholar
  12. 12.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29, 51–59 (1996)CrossRefGoogle Scholar
  13. 13.
    Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Proceedings of 12th International Conference on Pattern Recognition, vol. 1, pp. 582–585. IEEE Computer Society Press (1999)Google Scholar
  14. 14.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24, 971–987 (2002)CrossRefGoogle Scholar
  15. 15.
    Pietikäinen, M., Ojala, T., Xu, Z.: Rotation-invariant texture classification using feature distributions. Pattern Recogn. 33, 43–52 (2000)CrossRefGoogle Scholar
  16. 16.
    Mäenpää, T., Pietikäinen, M.: Classification with color and texture: jointly or separately? Pattern Recogn. 37, 1629–1640 (2004)CrossRefGoogle Scholar
  17. 17.
    Davarpanah, S.H., Khalid, F., Nurliyana Abdullah, L., Golchin, M.: A texture descriptor: background local binary pattern (BGLBP). Multimed. Tools Appl. 75, 6549–6568 (2016)CrossRefGoogle Scholar
  18. 18.
    Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with center-symmetric local binary patterns. In: Kalra, P.K., Peleg, S. (eds.) ICVGIP 2006. LNCS, vol. 4338, pp. 58–69. Springer, Heidelberg (2006).  https://doi.org/10.1007/11949619_6CrossRefGoogle Scholar
  19. 19.
    Liao, S., Zhao, G., Kellokumpu, V., Pietikainen, M., Li, S.Z.: Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1301–1306. IEEE (2010)Google Scholar
  20. 20.
    Papakostas, G.A., Koulouriotis, D.E., Karakasis, E.G., Tourassis, V.D.: Moment-based local binary patterns: a novel descriptor for invariant pattern recognition applications. Neurocomputing 99, 358–371 (2013)CrossRefGoogle Scholar
  21. 21.
    Brahnam, S., Jain, L.C., Nanni, L., Lumini, A. (eds.): Local Binary Patterns: New Variants and New Applications. Studies in Computational Intelligence, vol. 506. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-39289-4CrossRefGoogle Scholar
  22. 22.
    Silva, C., Bouwmans, T., Frélicot, C.: An extended center-symmetric local binary pattern for background modeling and subtraction in videos. In: Proceedings of the 10th International Conference on Computer Vision Theory and Applications, pp. 395–402 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Eftichia Badeka
    • 1
  • Theofanis Kalabokas
    • 1
  • Konstantinos Tziridis
    • 1
  • Alexander Nicolaou
    • 1
  • Eleni Vrochidou
    • 1
  • Efthimia Mavridou
    • 1
  • George A. Papakostas
    • 1
    Email author
  • Theodore Pachidis
    • 1
  1. 1.HUman-MAchines INteraction Laboratory (HUMAIN-Lab), Department of Computer ScienceInternational Hellenic UniversityKavalaGreece

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