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Detection of Young Green Apples in Orchard Environment Using Adaptive Ratio Chromatic Aberration and HOG-SVM

  • Xia XueEmail author
  • Zhou Guomin
  • Qiu Yun
  • Li Zhuang
  • Wang Jian
  • Hu Lin
  • Fan Jingchao
  • Guo Xiuming
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 545)

Abstract

It is still a challenge for fruit robot to automatic detecting young green apples in a complex grove environment due to color similarity with the background and varying illumination conditions. The purpose of this study was developing a robust method to detect young green apples in the tree canopy from low-cost color images acquired with diverse fruit sizes and under varying light circumstances. Adaptive green and blue chromatic aberration map was designed and combined with the iterative threshold segmentation algorithm to detect the region of interest contains potential apple fruits pixels. Then every potential fruit was identified by using an improved circular Hough transformation after morphological operation and blob analysis of the ITS outs which kept as many potential apple fruits pixels as possible. Finally, a kernel support vector machine classifier optimized by using grid search algorithm was built and combined with histogram of oriented gradients feature descriptor to distinguish and remove false fruit objects. The experimental result shows that the proposed method has better detection performance for young green apples.

Keywords

Young fruit Green apple Chromatic aberration map HOG-SVM 

Notes

Acknowledgements

This work was supported by a grant from the National High-tech R&D Program of China (863 Program No.2013AA102405) and Agricultural Science and Technology Innovation Project of Chinese Academy of Agricultural Sciences (Project No. CAAS-ASTIP-2016- AII).

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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Xia Xue
    • 1
    Email author
  • Zhou Guomin
    • 1
  • Qiu Yun
    • 1
  • Li Zhuang
    • 2
  • Wang Jian
    • 1
  • Hu Lin
    • 1
  • Fan Jingchao
    • 1
  • Guo Xiuming
    • 1
  1. 1.Institute of Agricultural Information, Chinese Academy of Agricultural SciencesBeijingChina
  2. 2.Institute of Pomology, Chinese Academy of Agricultural SciencesXingchengChina

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