Automatic Segmentation of Crop/Background Based on Luminance Partition Correction and Adaptive Threshold

Juan Liao, Yao Wang, Dequan Zhu, Yu Zou, Shun Zhang, Huiyu Zhou*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
33 Downloads (Pure)


Crop segmentation is a fundamental step of extracting the guidance line for an automated agricultural machine with a visual navigation system. However, the segmentation quality of green crop is seriously affected by the outdoor lighting conditions. To improve the accuracy of crop segmentation under complex lighting conditions, a color-index-based crop segmentation method with luminance partition correction and adaptive thresholding is proposed in this article. The method extracts the luminance component from the given RGB image and employs two adaptive thresholds to divide the luminance image into the dark, normal and bright areas. Then, a partition Gamma function is adaptively selected to improve the brightness levels of the dark and bright regions in which the Gamma correction parameter is adaptively determined based on the local distribution characteristics of illumination, and the corrected image is converted to the RGB counterpart through color saturation restoration. Finally, the ExG (excess green index) color index with Otsu thresholding is used to perform pre-segmentation in order to calculate the segmentation threshold for the final segmentation. Experimental results show that the proposed approach can effectively increase the brightness levels of the dark region and decrease the brightness levels in the bright region. In addition, compared with the traditional Otsu method employed in before and after luminance correction, the proposed method can achieve better segmentation results.

Original languageEnglish
Article number9249388
Pages (from-to)202611-202622
Number of pages12
JournalIEEE Access
Publication statusPublished - 05 Nov 2020

Bibliographical note

Funding Information:
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFD0700304, in part by the Science and Technology Major Program of Anhui Province under Grant 18030701204, and in part by the Key Research and Development Program of Anhui Province under Grant 202004a06020016 and Grant 1804a07020111.

Publisher Copyright:
© 2013 IEEE.

Copyright 2020 Elsevier B.V., All rights reserved.


  • adaptive threshold
  • Crop segmentation
  • Gamma correction
  • index-based segmentation
  • luminance partition

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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