Advertisement

DETECTION AND POSITION METHOD OF APPLE TREE IMAGE

  • Wenhua Mao
  • Baoping Jia
  • Xiaochao Zhang
  • Xiaoan Hub
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 294)

Abstract

Apples should be quickly and correctly detected from their surroundings for the apple harvesting robot. The basic color feature was extracted from FuJi apple tree images and analyzed by the statistical analysis method. Accordingly, a new apple detection method was proposed to position the centroid of picking apples. The color difference was used to segment apples from their surroundings. Then the picking apples were chosen by area parameter. After that, the conglutinated apples were segmented by bidirectional scanning line algorithm. Finally, all of picking apples were positioned by their circumdiameter matching algorithm. The experimental result showed that the correct classification rate of apple fruit achieved 90%.

Keywords

Color Indexing Apple Fruit Correct Classification Rate Apple Region Agricultural Mechanization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. A. Plebe, G. Grasso. Localization of spherical fruits for robotic harvesting, Machine Vision and Applications, 2001, 13 (2): 70–79CrossRefGoogle Scholar
  2. A. R. Jiménez, R. Ceres, J. L. Pons. A survey of computer vision methods for locating fruit on trees, Trans. ASAE, 2000, 43(6): 1911–1920Google Scholar
  3. D. M. Bulanon, T. Kataoka, S. Zhang, Y. Ota, T. Hiroma. Optimal thresholding for the automatic recognition of apple fruits, ASAE Paper, 2001, No. 01–3133Google Scholar
  4. D. M. Bulanon, T. Kataoka, Y. Ota, and T. Hiroma. A Color Model for Recognition of Apples by a Robotic Harvesting System, Journal of the JSAM, 2002, 64(5):123–133Google Scholar
  5. D. Stanjnko, M. Lakota, M. Hocevar. Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging, Computers and Electronics in Agr, 2004, 42: 31–42CrossRefGoogle Scholar
  6. J. Zhao, J. Tow, J. Katupitiya. On-tree fruit recognition using texture properties and color data,IEEE/RSJ Int. Conf. Intell. Robots and Systems, 2005, 263–268Google Scholar
  7. L. T. Amy, L. P.Donald, P. Johnny. Segmentation of Apple Fruit from Video via Background Modeling, ASAE Paper, 2006, No. 063060Google Scholar
  8. T. Takahashi, S. Zhang, H. Fukuchi. Measurement of 3-D Locations of Fruit by Binocular Stereo Vision for Apple Harvesting in an Orchard, ASAE Paper, 2002, No. 021102Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Wenhua Mao
    • 1
    • 2
  • Baoping Jia
    • 1
  • Xiaochao Zhang
    • 2
  • Xiaoan Hub
    • 2
  1. 1.China Agricultural UniversityBeijingChina
  2. 2.Chinese Academy of Agricultural Mechanization SciencesBeijingChina

Personalised recommendations