A Computerized Vision - Based Method for Automatic Detection of Tea Shoot Tips

Received: 22-07-2015

Accepted: 03-09-2015

DOI:

Views

0

Downloads

0

Section:

KỸ THUẬT VÀ CÔNG NGHỆ

How to Cite:

Thuy, N., Hai, V., Huyen, N., & LanAnh, P. (2024). A Computerized Vision - Based Method for Automatic Detection of Tea Shoot Tips. Vietnam Journal of Agricultural Sciences, 13(6), 968–975. http://testtapchi.vnua.edu.vn/index.php/vjasvn/article/view/1530

A Computerized Vision - Based Method for Automatic Detection of Tea Shoot Tips

Nguyen Thi Thuy (*) 1 , Vu Hai 1 , Nguyen Thi Huyen 1 , Pham Thi LanAnh 1

  • 1 Khoa Công nghệ Thông tin, Học viện Nông nghiệp Việt Nam
  • Abstract


    For tea producers and tea researchers, counting tender shoots in a samplingarea is usually done to evaluate productivity, recordthe growth of teaor to decide the appropriate time for harvest. However, it is a tedious and time consuming task. In this paper, we proposeda computerized vision - based method for automatically detecting and counting the number of tea shoottips in an image acquired from a tea field. First, we builta parametric model of a tea – shoottipscolor distribution in order to roughly separate Regions - of - Interest (ROIs) from a complicated background. For each ROI, we then extractedsupportive (local) features with expectations that these features will only appear around an apical bud of tea shoots thanks to two measurements: the density of edge pixels and thestatistic of gradient directions. Consequently, the extracted features were put into a mean shift cluster to locate the position of tea shoottips. The proposed method wasevaluated on a set of testing images at different sitesof tea fields and different plant ages. The results showed that the system could recognize tea shoot tips with86% accuracy. It is, therefore, possibleto designacounting-assistedtool for supportingtea producers or tea researchers.

    References

    Canny, J. (1986). A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE T-PAMI, 8: 679 - 698.

    Golzarian R.M., J. Cai, R. Frick, and S. Miklavcic (2011). Segmentation of cereal plant images using level set methods a comparative study. Journal of Infor mation and Electronics Engineering, p. 72 - 78.

    Gonzalez, R., and R. Woods, (2008). Digital Image Processing: Pearson Prentice Hall.

    Kumar Neeraj, Peter N. Belhumeur, Arijit Biswas, David W. Jacobs, W. John Kress, Ida C. Lopez, João V. B. Soares (2012). “Leafsnap: A computer vision system for automatic plant species identification”. Proceedings of the 12th European Conference on Computer Vision, vol. LNCS 7584: 502 - 516.

    Wang, J., X.Zeng, and J. Liu (2011). Three - dimensional modeling of tea - shoots using images and models. Sensors, 11(4): 3803 - 3815.

    Wang, X. - F., D. - S. Huang, ,J. - X. Du, , H. Xu, , and L. Heutte (2008). Classification of plant leaf images with complicated background. Applied mathematics and computation, 205(2): 916 - 926.

    Zhiyi, H. C. C. Quansheng, and C. Jianrong (2012). Identification of green tea (Camellia sinensis) quality level using computer vision and pattern recognition. Proceedings of the 2012 International Conference on Biological and Biomedical Sciences, p. 20 - 28