Segmentation of Rumex obtusifolius using Gaussian Markov random fields

S. Atni Hiremath, V.A. Tolpekin, G. van der Heijden, A. Stein

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)


Rumex obtusifolius is a common weed that is difficult to control. The most common way to control weeds-using herbicides-is being reconsidered because of its adverse environmental impact. Robotic systems are regarded as a viable non-chemical alternative for treating R. obtusifolius and also other weeds. Among the existing systems for weed control, only a few are applicable in real-time and operate in a controlled environment. In this study, we develop a new algorithm for segmentation of R. obtusifolius using texture features based on Markov random fields that works in real-time under natural lighting conditions. We show its performance by comparing it with an existing real-time algorithm that uses spectral power as texture feature. We show that the new algorithm is not only accurate with detection rate of 97.8 % and average error of 56 mm in estimating the location of the tap-root of the plant, but is also fast taking just 0.18 s to process an image of size pixels making it feasible for real-time applications.
Original languageEnglish
Pages (from-to)845-854
JournalMachine Vision Applications
Issue number4
Publication statusPublished - 2013


  • energy minimization
  • texture features
  • weed-control
  • graph cuts
  • classification
  • systems
  • imagery
  • vision


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