Advertisement

A New Approach for Measuring Leaf Projected Area for Potted Plant Based on Computer Vision

  • Kaiyan Lin
  • Huiping SiEmail author
  • Jie Chen
  • Junhui Wu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 634)

Abstract

Leaves are the main organs for plant photosynthesis and transpiration, and thus accurate and rapid measurements of their surface area are of great significance in plant growth studies. A novel method was developed to utilize computer vision to determine the leaf projected area for potted plants using a reference object. During leaf extraction, the excess green vegetation index and the Otsu algorithm were used for image binarization. Blob analysis was then performed to eliminate noise and extract leaf images. After grey level transform and obtaining the binary image, the Sobel operator and Hough transform were utilized to detect pot’s external boundary. The image including the extracted leaves and the detected reference borders were then processed using a Geometric correction to improve the measurement accuracy, and based on this, the leaf projected area was calculated. Results of the experimental image showed that this newly proposed method proved is effective and can continuously measure the plant’s leaf area for non-destructive monitoring.

Keywords

Image segmentation Edge detection Hough transform Projected area Image correction 

Notes

Acknowledgment

This work was supported by Grant No. 2014BAD05B05 from the Ministry of Science and technology of China and the fundamental research funds for the central universities of China.

References

  1. 1.
    Breda, N.J.: Ground-based measurements of leaf area index, a review of methods: instruments and current controversies. J. Exp. Bot. 54(11), 2403–2417 (2003)CrossRefGoogle Scholar
  2. 2.
    Xu, R., Dai, J., Luo, W., et al.: A photothermal model of leaf area index for greenhouse crops. Agric. For. Meteorol. 150, 541–552 (2010)CrossRefGoogle Scholar
  3. 3.
    Igathinathane, C., Prakash, V.S.S., Padma, U.: Interactive computer software development for leaf area measurement. Comput. Electron. Agric. 51, 1–16 (2006)CrossRefGoogle Scholar
  4. 4.
    Cho, Y.Y., Oh, S.B., Oh, M.M., Jung, E.S.: Estimation of individual leaf area, fresh weight, and dry weight of hydroponically grown cucumbers (Cucumis sativus L.) using leaf length, width, and SPAD value. Sci. Hortic. 111, 330–334 (2007)CrossRefGoogle Scholar
  5. 5.
    Trooien, T.P., Heermann, D.R.: Measurement and simulation of potato leaf area using image processing III. Measur. Trans. ASAE 35(5), 1719–1721 (1992)CrossRefGoogle Scholar
  6. 6.
    Kacira, M., Ling, P.P., Short, T.H.: Machine vision extracted plant movement for early detection of plant water stress. Trans. ASAE 45(4), 1147–1153 (2002)CrossRefGoogle Scholar
  7. 7.
    Story, D., Kacira, M., Kubota, C., Akoglu, A., An, L.L.: Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments. Comput. Electron. Agric. 74, 238–243 (2010)CrossRefGoogle Scholar
  8. 8.
    Xia, Y.W., Xu, D.Y., Du, J.S.: On-line measurement of tobacco leaf area based on machine vision. Trans. Chin. Soc. Agric. Mach. 43(10), 167–173 (2012)Google Scholar
  9. 9.
    Tong, J.H., Li, J.B., Jiang, H.Y.: Machine vision techniques for the evaluation of seedling quality based on leaf area. Biosyst. Eng. 115, 369–379 (2013)CrossRefGoogle Scholar
  10. 10.
    Kataoka, T., Kaneko, T., Okamoto, H., Hata, S.I.: Crop growth estimation system using machine vision. In: Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 1079–1083 (2003)Google Scholar
  11. 11.
    Meyer, G.E., Neto, J.C.: Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 63, 282–293 (2008)CrossRefGoogle Scholar
  12. 12.
    Perez, A.J., Lopez, F., Benlloch, J.V., Christensen, S.: Color and shape analysis techniques for weed detection in cereal fields. Comput. Electron. Agric. 25, 197–212 (2000)CrossRefGoogle Scholar
  13. 13.
    Woebbecke, D.M., Meyer, G.E., Bargen, K.V., Mortensen, D.A.: Color indices for weed identification under various soil residue and lighting conditions. Trans. ASAE 38, 259–269 (1995)CrossRefGoogle Scholar
  14. 14.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.Modern Agricultural Science and Engineering Institute of Tongji UniversityShanghaiChina

Personalised recommendations