Skip to main content
SpringerLink
Log in

A Type-2 Fuzzy Clustering and Quantum Optimization Approach for Crops Image Segmentation

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

Automatic detection of crop yield ripeness is a tedious task because of the presence of various intensities of color in crops. One of the solutions to this problem is the monitoring of those crops by performing segmentation operations. This operation can help to distinguish the ripe and non-ripe regions among the crop images. For this purpose, this study presents a new hybrid crop image segmentation method utilizing type-2 fuzzy set (T2FS), K-means clustering algorithm, and modified quantum optimization algorithm (MQOA). The proposed method fully utilizes the indispensable qualities of these three techniques by (a) using T2FS to represent each color component of crop images in terms of secondary memberships, (b) applying K-means clustering algorithm to extract the similar features from the set of type-2 entropy values obtained from the secondary memberships, and (c) exploiting MQOA to optimize the distance function used in K-means clustering algorithm to obtain the optimal clusters. The performance of the proposed method is assessed based on the experiments carried out on color images of cherry tomatoes. Evidence of experimental results suggests that the proposed method produces extremely effective segmented images relative to those well-known color image segmentation methods available in the literature of pattern recognition and computer vision domains.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. C. S. Division, Taiwan Agriculture Research Institute (June 2020). https://www.tari.gov.tw/english/.

  2. Yu, Z., Cao, Z., Wu, X., Bai, X., Qin, Y., Zhuo, W., Xiao, Y., Zhang, X., Xue, H.: Automatic image-based detection technology for two critical growth stages of maize: emergence and three-leaf stage. Agric. For. Meteorol. 174–175, 65–84 (2013)

    Article  Google Scholar 

  3. Cuevas-Velasquez, H., Gallego, A.-J., Fisher, R.B.: Segmentation and 3D reconstruction of rose plants from stereoscopic images. Comput. Electron. Agric. 171, 1–18 (2020)

    Article  Google Scholar 

  4. Rubio, J.J., Kashiwa, T., Laiteerapong, T., Deng, W., Nagai, K., Escalera, S., Nakayama, K., Matsuo, Y., Prendinger, H.: Multi-class structural damage segmentation using fully convolutional networks. Comput. Ind. 112, 103–121 (2019)

    Article  Google Scholar 

  5. Cheng, H.-D., Jiang, X., Wang, J.: Color image segmentation based on homogram thresholding and region merging. Pattern Recognit. 35(2), 373–393 (2002)

    Article  MATH  Google Scholar 

  6. Hemming, J., Rath, T.: PA-precision agriculture: computer-vision based weed identification under field conditions using controlled lighting. J. Agric. Eng. Res. 78(3), 233–243 (2001)

    Article  Google Scholar 

  7. Tellaeche, A., Burgos-Artizzu, X.P., Pajares, G., Ribeiro, A.: A vision-based method for weeds identification through the Bayesian decision theory. Pattern Recognit. 41(2), 521–530 (2008)

    Article  MATH  Google Scholar 

  8. Bai, X.D., Cao, Z.G., Wang, Y., Yu, Z.H., Zhang, X.F., Li, C.: Crop segmentation from images by morphology modeling in the CIE l_a_b_ color space. Comput. Electron. Agric. 99, 21–34 (2013)

    Article  Google Scholar 

  9. Shrestha, D.S., Steward, B., Birrell, S.: Video processing for early stage maize plant detection. Biosyst. Eng. 89(2), 119–129 (2004)

    Article  Google Scholar 

  10. Wang, X.-Y., Wang, Q.-Y., Yang, H.-Y., Bu, J.: Color image segmentation using automatic pixel classification with support vector machine. Neurocomputing 74(18), 3898–3911 (2011)

    Article  MATH  Google Scholar 

  11. Guerrero, J., Pajares, G., Montalvo, M., Romeo, J., Guijarro, M.: Support vector machines for crop/weeds identification in maize fields. Expert Syst. Appl. 39(12), 11149–11155 (2012)

    Article  Google Scholar 

  12. Fritz, H., García-Escudero, L.A., Mayo-Iscar, A.: Robust constrained fuzzy clustering. Inf. Sci. 245, 38–52 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  13. Yao, H., Duan, Q., Li, D., Wang, J.: An improved K-means clustering algorithm for fish image segmentation. Math. Comput. Model. 58(3–4), 790–798 (2013)

    Article  MATH  Google Scholar 

  14. Bai, X., Cao, Z., Wang, Y., Yu, Z., Hu, Z., Zhang, X., Li, C.: Vegetation segmentation robust to illumination variations based on clustering and morphology modelling. Biosyst. Eng. 125, 80–97 (2014)

    Article  Google Scholar 

  15. Pare, S., Kumar, A., Bajaj, V., Singh, G.K.: A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve. Appl. Soft Comput. 47, 76–102 (2016)

    Article  Google Scholar 

  16. He, L., Huang, S.: Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing 240, 152–174 (2017)

    Article  Google Scholar 

  17. Abbasgholipour, M., Omid, M., Keyhani, A., Mohtasebi, S.: Color image segmentation with genetic algorithm in a raisin sorting system based on machine vision in variable conditions. Expert Syst. Appl. 38(4), 3671–3678 (2011)

    Article  Google Scholar 

  18. Li, K., Tan, Z.: An improved flower pollination optimizer algorithm for multilevel image thresholding. IEEE Access 7, 165571–165582 (2019)

    Article  Google Scholar 

  19. Tan, K.S., Isa, N.A.M.: Color image segmentation using histogram thresholding—fuzzy c-means hybrid approach. Pattern Recognit. 44(1), 1–15 (2011)

    Article  MATH  Google Scholar 

  20. Gao, Y., Wang, D., Pan, J., Wang, Z., Chen, B.: A novel fuzzy c-means clustering algorithm using adaptive norm. Int. J. Fuzzy Syst. 21(8), 2632–2649 (2019)

    Article  Google Scholar 

  21. Le, T., Huynh, T., Lin, L., Lin, C., Chao, F.: A K-means interval type-2 fuzzy neural network for medical diagnosis. Int. J. Fuzzy Syst. 21(7), 2258–2269 (2019)

    Article  Google Scholar 

  22. Meyer, G.E., Neto, J.C., Jones, D.D., Hindman, T.W.: Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Comput. Electron. Agric. 42(3), 161–180 (2004)

    Article  Google Scholar 

  23. Ciesielski, K.C., Udupa, J.K.: Affinity functions in fuzzy connectedness based image segmentation I: equivalence of affinities. Comput. Vis. Image Underst. 114(1), 146–154 (2010)

    Article  Google Scholar 

  24. Lan, J., Zeng, Y.: Multi-threshold image segmentation using maximum fuzzy entropy based on a new 2D histogram. Optik 124(18), 3756–3760 (2013)

    Article  Google Scholar 

  25. Mendel, J.M., John, R.I.B.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2), 117–127 (2002)

    Article  Google Scholar 

  26. Clairet, J., Bigand, A., Colot, O.: Color image segmentation using type-2 fuzzy sets. In: Proceedings of 1st IEEE International Conference on E-Learning in Industrial Electronics, Hammamet, Tunisia, December 2006, pp. 52–57

  27. Miguel, L.D., Santos, H., Sesma-Sara, M., Bedregal, B., Jurio, A., Bustince, H., Hagras, H.: Type-2 fuzzy entropy sets. IEEE Trans. Fuzzy Syst. 25(4), 993–1005 (2017)

    Article  Google Scholar 

  28. Mújica-Vargas, D., Kinani, J.M.V., Rubio, J.D.: Color-based image segmentation by means of a robust intuitionistic fuzzy c-means algorithm. Int J. Fuzzy Syst. 22, 901–916 (2020)

    Article  Google Scholar 

  29. Fazel Zarandi, M.H., Khadangi, A., Karimi, F., Turksen, I.B.: A computer-aided type-II fuzzy image processing for diagnosis of meniscus tear. J. Digit. Imaging 29(6), 677–695 (2016)

    Article  Google Scholar 

  30. Castillo, O., Sanchez, M.A., Gonzalez, C.I., Martinez, G.E.: Review of recent type-2 fuzzy image processing applications. Information 8(97), 1–18 (2017)

    Google Scholar 

  31. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  32. Huang, Y.-P., Singh, P., Kuo, H.-C.: A hybrid fuzzy clustering approach for the recognition and visualization of MRI images of Parkinson’s disease. IEEE Access 27, 25041–25051 (2020)

    Article  Google Scholar 

  33. Schrodinger, E.: The present status of quantum mechanics. Die Naturwissenschaften 23(48), 1–26 (1935)

    Article  Google Scholar 

  34. Singh, P., Huang, Y.-P.: A new hybrid time series forecasting model based on the neutrosophic set and quantum optimization algorithm. Comput. Ind. 111, 121–139 (2019)

    Article  Google Scholar 

  35. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  36. Unnikrishnan, R., Pantofaru, C., Hebert, M.: A measure for objective evaluation of image segmentation algorithms. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, vol. 3, January 2005, pp. 34–41

  37. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of 8th IEEE International Conference on Computer Vision, Vancouver, BC, Canada, vol. 2, July 2001, pp. 416–423

  38. Meila, M.: Comparing clusterings—an information based distance. J Multivar. Anal. 98(5), 873–895 (2007)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This study was funded by the Ministry of Science and Technology, Taiwan, under Grants MOST108-2321-B-027-001-, MOST108-2221-E-027-111-MY3, and MOST109-2622-E-027-001-CC3.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Taipei University of Technology, Taipei, 10608, Taiwan

    Yo-Ping Huang, Pritpal Singh & Wen-Lin Kuo

  2. Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung, 41349, Taiwan

    Yo-Ping Huang & Hung-Chi Chu

Authors
  1. Yo-Ping Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pritpal Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen-Lin Kuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hung-Chi Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yo-Ping Huang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, YP., Singh, P., Kuo, WL. et al. A Type-2 Fuzzy Clustering and Quantum Optimization Approach for Crops Image Segmentation. Int. J. Fuzzy Syst. 23, 615–629 (2021). https://doi.org/10.1007/s40815-020-01009-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-020-01009-2

Keywords

Search

Navigation