Journal of Forestry Research

, Volume 13, Issue 3, pp 245–249 | Cite as

Review on the proceeding of automatic seedlings classification by computer vision

  • Yang Yan-zhu
  • Zhao Xue-zeng
  • Wang Wei-jie
  • Wu Xian


The classification of seedlings is important to ensure the viability of seedlings after transplantation and is acknowledged as a key factor in forestation and environmental improvement. Based on numerous papers on automatic seedling classification (ASC), the seedling grading theory, traditional grading methods, the background and the proceeding of ASC techniques are described. The automation of the measurement of seedling morphological characteristics by photoelectric meters and computer vision is studied, and the automatic methods of the current grading systems are described respectively. And the further researches on ASC by computer vision are proposed.


Seedlings classification Automation Morphological characteristic Computer vision 

CLC number


Document code


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ardalan, S.H., Hassan, A.E. 1982. Automatic feeding and sorting of bare root seedlings [J]. Transactions of the ASEA,25(2): 266–270.Google Scholar
  2. Bai Jingfeng, Zhao Xuezeng, Qiang Xifuet al. 2000. Study on extraction of computer vision features of conifer seedling [J]. Journal of Northeast Forestry University,28(5): 94–96.Google Scholar
  3. Bai Jingfeng, Zhao Xuezeng, Qiang Xifuet al. 2000. Study on the automatic grading system of conifer seedling [J]. Forestry Machinery and Woodworking Equipment,28(8): 9–11.Google Scholar
  4. Bai Jingfeng, Zhao Xuezeng, Qiang Xifuet al. 2001. Edge detection based on fuzzy gradient method [J]. Control and Decision,16(3): 351–354.Google Scholar
  5. Bukley, D.J., Rerd, W.S., Armson, K.A. 1978. A digital recording system for measuring root area and dimensions of tree seedling [J]. Transactions of the ASAN,21(2): 222–226.Google Scholar
  6. Gasvoda, D. 1994. Machine vision computerized sorting and grading system for tree seedling [R]. Timber tech tips, Missoula Technology Development Center, USDA Forest Service.Google Scholar
  7. Guo Junfeng, Cai Yuanlong. 1994. Study on 3D image reconstruction basing on morphology interpolation [J]. Journal of Xi’an JiaoTong University,28(2): 109–114.Google Scholar
  8. Hassan, A.E., Tohinaz, A.S., Roise, J.P. 1992. Evaluation of manual sorting in three pine nurseries [J]. Transactions of the ASAE,35(6): 1981–1986.Google Scholar
  9. Howarth, M.S., Stanwood, P.C. 1992. Measurement of seedling growth rate by machine vision [C]. Optics in Agriculture, Forestry, and Biological Processing, Proceedings of SPIE, Vol.1836, pp. 185–194.Google Scholar
  10. Joongho Chang, Gunhee Han, Valverde, J.M. 1997. Cork quality classification system using a unified image processing and fuzzy-neural network methodology [J]. Transactions on Neural Networks,8(4): 964–973.PubMedCrossRefGoogle Scholar
  11. Kutz, L.J., Wlihoit, J.H., Fly, D.E. 1993. Multiple camera machine vision system for pine seedlings measurements [R]. ASEA Paper No. 93-3028, ASAE, St. Joseph, MI 49085.Google Scholar
  12. Lebowitz, R.J. 1988. Digital analysis measurement of root length and diameter [J]. Environmental and Experimental Botany,28(3): 267–273.CrossRefGoogle Scholar
  13. Li Qingzhong, Zhang Man, Wang Maohua. 2000. Real-time apple color grading based genetic neural network [J]. Journal of Image and Graphics,5(9): 779–789.Google Scholar
  14. Liu Zhexing, Li Shuxiang, Lu Qingwen. 1999. A directional interpolation method for 3D gray-scale image based on local plane information [J]. Beijing Biomedical Engineering,18(4): 216–220.Google Scholar
  15. M.P. Ringey, G.A. Kranzler. 1989. “Performance of a machine vision based tree seedling grader” [R]. ASAE Paper NO. 89-3007, ASAE, St. Joseph, MI 49085Google Scholar
  16. Miller, B.K., Delwiche, M.J. 1989. A color vision system for peach grading [J]. Tranaction of the ASAE,32(4): 1484–1490.Google Scholar
  17. Morrison, I.K., Armson, K.A. 1968. The rhizometer-adevice for measuring roots of tree seedling [C]. The Forestry Chroaide, 21–23.Google Scholar
  18. Neuman, M.R., Sapirstein, H.D., Shwedyk, al. 1989. Wheat grain color analysis by digital image processing [J]. Wheat Class Discrimantion. Journal of Cereal Science,10: 183–188.Google Scholar
  19. Ringey, M.P., Kranzler, G.A. 1997. Neural network recognition of the conifer seedling root collar [C]. Optics in Agriculture and Forestry, Proceedings of SPIE, Vol.2907, pp. 109–118.Google Scholar
  20. Ringey, M.P., Kranzler, G.A. 1988. Machine vision for grading southern pine seedlings [J]. Transactions of the ASAN,31(2): 642–646.Google Scholar
  21. Ringey, M.P., Kranzler, G.A. 1992. Line-scan inspection of conifer seedlings [C]. Optics in Agriculture and Forestry, Proceedings of SPIE, Vol.1836, pp. 166–174.Google Scholar
  22. Ringey, M.P., Kranzler, G.A. 1994. Machine vision for measuring conifer seedlings morphology [C]. Optics in Agriculture, Forestry, and Biological Processing, Proceedings of SPIE, Vol.2345, pp. 26–35.Google Scholar
  23. Ruzhitsky, V., Ling, P. P. 1992. Image analysis for tomato seedling grading [R]. ASAE Paper No. 92-3588, St. Joseph, MI.Google Scholar
  24. Schubert Erhard, Rath, H., Klicker Juergen. 1994. Fast 3D object recognition using a combination of color-coded phase-shift principle and color-coded triangulation [C]. Proceedings of SPIE—The International Society for Optical Engineering,2247: 202–213.Google Scholar
  25. Suh, S.R., Miles, G.E. 1988. Measurement of morphological properties of tree seedlings using machine vision and image processing [R]. ASAE Paper No. 88-1542, St. Joseph, MI.Google Scholar
  26. Tao Y., Heinemann P.H., Varghese, al. 1995. Machine vision for color inspection of potatoes and apples [J]. Tranaction of the ASAE,38(5): 1555–1561.Google Scholar
  27. Wilhot, J.H., Kutz, L.J., Fly, D.E., South, D.B. 1994. PC-based multiple camera machine vision systems for pine seedling measurement [J]. Applied Engineering in Agriculture,34(4): 48–52.Google Scholar
  28. Wilhot, J.H., Kutz, L.J., Vandiver, W.A. 1994. Machine vision system for quality control assessment of bare root pine seedlings [C]. Optics in Agriculture, Forestry, and Biological Processing, Proceedings of SPIE, Vol.2345, pp. 36–49.Google Scholar
  29. Woebbecke, D.M., Meyer, G.E., Bargen, K.V. 1992. Plant species identification, size, and enumeration using machine vision techniques on near-binary images [J]. Optics in Agriculture and Forestry,1836: 208–218.Google Scholar
  30. Wu Xian, Bai jingfeng, Lin Balanet al. 1998. System of automatic grading of conifer seedling by computer vision [J]. Journal of Northeast Forestry University,26(4): 32–35.Google Scholar
  31. Zhou Jian, Zhao Mingtao, Yang Yuxiao. 1999. Study on the multiscale binary-wavelet based edge detection for layer-by-layer 3D profilometry image [J]. China Mechanical Engineering,10(11): 1242–1246.Google Scholar

Copyright information

©  0271 0304 V 2 2002

Authors and Affiliations

  • Yang Yan-zhu
    • 1
  • Zhao Xue-zeng
    • 1
  • Wang Wei-jie
    • 1
  • Wu Xian
    • 1
  1. 1.Harbin Institute of TechnologyHarbinP. R. China

Personalised recommendations