A Separating Method of Adjacent Apples Based on Machine Vision and Chain Code Information

  • Juan Feng
  • Shengwei Wang
  • Gang Liu
  • Lihua Zeng
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 368)

Abstract

Fruit location is an important parameter for apple harvesting robot to conduct picking task. However, it is difficult to obtain coordinates of each apple under natural conditions. One of the major challenges is detecting adjacent fruits accurately. Previous studies for adjacent detection have shortcomings such as vast computation, difficulty in implementation and over-segmentation. In this paper, we propose a novel and effective separating method for adjacent apples recognition based on chain code information and obtain the centroid coordinates of each fruit. Firstly, those valid regions of fruit are extracted by pre-processing the initial image. Secondly, chain code information is obtained by following the contour of extracted regions. Thirdly, through observing the changing law of chain code difference and adopting local optimum principle, concave points are found. Finally, the best point pairs are determined with different matching principles, and those adjacent apples are separated exactly. The experimental results show that the average rate of successful separation is greater than 91.2% with the proposed method, which can meet the requirements of applications in harvesting robots.

Keywords

Separating method Chain code difference Concave point Match principle Local optimum 

References

  1. 1.
    Bulanon, D.M., Kataoka, T., Ota, Y., Hiroma, T.: A Color Model for Recognition of Apples by a Robotic Harvesting System. Journal of the JSAM 64(5), 123–133 (2002)Google Scholar
  2. 2.
    Stajnko, D., Cmelik, Z.: Modelling of Apple Fruit Growth by Application of Image Anlaysis. Agricultura Conspectus Scientificus 70(2), 59–64 (2005)Google Scholar
  3. 3.
    Zhao, J., Tow, J., Katupitiya, J.: On-tree Fruit Recognition Using Texture Properties and Color Data. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3993–3998. IEEE Press, New York (2005)Google Scholar
  4. 4.
    Zhou, T.J., Zhang, T.Z., Yang, L.: Comparison of Two Algorithms Based on Mathematical Morphology for Segmentation of Touching Strawberry Fruits. Transactions of the CSAE 23(9), 164–168 (2007) (in Chinese)MathSciNetGoogle Scholar
  5. 5.
    Luengo-Oroz, M.A., Faure, E., Angulo, J.: Robust iris Segmentation on Uncalibrated Noisy Images Using Mathematical Morphology. Image and Vision Computing 28, 278–284 (2009)CrossRefGoogle Scholar
  6. 6.
    Chinchuluun, R., Lee, W.S.: Citrus Yield Mapping System in Natural Outdoor Scenes Using the Watershed Transform. ASABE Paper No. 063010, St. Joseph, MI USA (2006)Google Scholar
  7. 7.
    Lee, W.S., Slaughter, D.C.: Recognition of Partially Occluded Plant Leaves Using a Modified Watershed Algorithm. Transactions of the ASAE 47(4), 1269–1280 (2004)Google Scholar
  8. 8.
    Wang, Y.C., Chou, J.J.: Automatic Segmentation of Touching Rice Kernels with an Active Contour model. Transactions of the ASABE 47(5), 1803–1811 (2005)Google Scholar
  9. 9.
    Zhang, Y.J., Li, M.Z., Liu, G.: Separating Adjoined Apples Based on Machine Vision and Information Fusion. Transactions of the Chinese Society for Agricultural Machinery 40(11), 180–183 (2009) (in Chinese) MathSciNetGoogle Scholar
  10. 10.
    Liu, W.H., Sui, Q.M.: Automatic Segmentation of Overlapping Powder Particle Based on Searching Concavity Points. Journal of electronic measurement and instrument 24(12), 1095–1100 (2010) (in Chinese) CrossRefGoogle Scholar
  11. 11.
    Annerel, E., Taerwe, L.: Methods to Quantify the Colour Development of Concrete Exposed to Fire. Construction and Building Materials 25(10), 3989–3997 (2011)CrossRefGoogle Scholar
  12. 12.
    Lak, M.B., Minaer, S., Amiriparian, J., Beheshti, B.: Apple Fruits Recognition Under Natural Luminance Using Machine Vision. Advance Journal of Food Science and Technology 2(6), 325–327 (2010)Google Scholar
  13. 13.
    Otsu, N.: A Threshold Selection Method from Gray-level Histograms. IEEE Transactions on System Man and Cybernetics 9(1), 62–69 (1979)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Bulanon, D.M., Kataoka, T.: A Fruit Detection System and an End Effector for Robotic for Robotic Harvesting of Fuji Apples. Agricultural Engineering International: the CIGR Ejournal 12(1), 1285–1298 (2010)Google Scholar
  15. 15.
    Gonzalez, R.C.: Digital Image Processing Using MATLAB. Publishing House of Electronics Industry (2005)Google Scholar
  16. 16.
    Liu, K., Fei, S.M., Wang, M.L.: Cotton Recognition Based on Randomized Hough Transform. Transactions of the Chinese Society for Agricultural Machinery 41(8), 160–165 (2010) (in Chinese) Google Scholar
  17. 17.
    Freeman, H.: Computer Processing of Line-drawing Date. Computer Surveys 6(1), 57–96 (1974)MATHCrossRefGoogle Scholar
  18. 18.
    Zhu, Y., Jiang, L.J., Xiao, Y.L.: Concave Spots Localization and Region Segmentation in Fibrous Material Image Based on Chain Codes. Journal of Nanjing University of Science and Technology (Natural Science) 32(1), 110–113 (2008) (in Chinese)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Juan Feng
    • 1
    • 2
  • Shengwei Wang
    • 1
    • 3
  • Gang Liu
    • 1
  • Lihua Zeng
    • 2
  1. 1.Key Laboratory of Modern Precision Agriculture System Integration ResearchMinistry of Education, China Agricultural UniversityBeijingChina
  2. 2.College of Information Science & TechnologyAgricultural University of HebeiBaodingChina
  3. 3.College of Mathematics and Information ScienceNorthwest Normal University of GansuLanzhouChina

Personalised recommendations