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Segmentation of Rumex obtusifolius using Gaussian Markov random fields

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Abstract

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 \(576 \times 432\) pixels making it feasible for real-time applications.

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Acknowledgments

The authors would like to thank Frits K van Evert from Plant Research International, Wageningen, for providing the images used in the study and for his contribution towards formulating the research objective.

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Correspondence to Santosh Hiremath.

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Hiremath, S., Tolpekin, V.A., van der Heijden, G. et al. Segmentation of Rumex obtusifolius using Gaussian Markov random fields. Machine Vision and Applications 24, 845–854 (2013). https://doi.org/10.1007/s00138-012-0470-0

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  • DOI: https://doi.org/10.1007/s00138-012-0470-0

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