Robust vegetation segmentation under field conditions using new adaptive weights for hybrid multichannel images based on the Chan-Vese model

Yamina Boutiche*, Abdelhamid Abdesselam, Nabil Chetih, Mohammed Khorchef, Naim Ramou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


This paper proposes a method for detecting vegetation in agricultural images under real field conditions. It includes two modules: The first module constructs a multichannel image by combining four color indices and the Lab color space using Principal Component Analysis (PCA). The second module detects the vegetation by applying an improved Chan-Vese method. In this method, the energy weights are automatically estimated based on the contrast between foreground regions and the background. To speed up the segmentation process a sweeping algorithm is applied. Experimental results demonstrate that our algorithm outperforms ten state-of-the-art methods, yielding higher accuracy, precision, and achieving better recall and F-score rates. The main advantage of the proposed method is that it performs well under different field conditions. On the seven datasets considered in this work, the proposed method achieved 97.10%,95.70%,95.70%, and 96.37% averages in terms of accuracy, F-score, precision, and recall respectively.

Original languageEnglish
Article number101850
Pages (from-to)101850
Number of pages1
JournalEcological Informatics
Publication statusPublished - Dec 1 2022


  • Active contours
  • Adaptive weights
  • Chan-Vese model
  • Fast optimization
  • Level sets
  • Vegetation segmentation

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Ecology
  • Modelling and Simulation
  • Ecological Modelling
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics

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