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.
- energy minimization
- texture features
- graph cuts
Atni Hiremath, S., Tolpekin, V. A., van der Heijden, G., & Stein, A. (2013). Segmentation of Rumex obtusifolius using Gaussian Markov random fields. Machine Vision Applications, 24(4), 845-854. https://doi.org/10.1007/s00138-012-0470-0