Mean-shift-based color segmentation of images containing green vegetation

Zheng, Li; Zhang, Jingtao; Wang, Qianyu

doi:10.1016/j.compag.2008.08.002

Cited by 142 publications

(81 citation statements)

References 19 publications

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“…Ruiz-Ruiz et al (2009) applied the EASA later under the HSI (hue-saturationintensity) color space to deal with the illumination variability. Zheng et al (2009Zheng et al ( , 2010) used a supervised mean-shift algorithm under the assumption that the segmentation of green vegetation from a background can be treated as a two-class segmentation problem; the class separability was validated through a neural network and the Fisher linear discriminant respectively, the color spaces used were RGB, LUV and HSI.…”

Section: Revision Of Methodsmentioning

confidence: 99%

Automatic segmentation of relevant textures in agricultural images

Guijarro

Pajares

Riomoros

et al. 2011

Computers and Electronics in Agriculture

297

122

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Keywords:Machine vision Image segmentation Texture identification in crops Automatic tasks in agriculture One important issue emerging strongly in agriculture is related with the automatization of tasks, where the optical sensors play an important role. They provide images that must be conveniently processed. The most relevant image processing procedures require the identification of green plants, in our experiments they come from barley and corn crops including weeds, so that some types of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations. Also the identification of textures belonging to the soil could be useful to know some variables, such as humidity, smoothness or any others. Finally, from the point of view of the autonomous robot navigation, where the robot is equipped with the imaging system, some times it is convenient to know not only the soil information and the plants growing in the soil but also additional information supplied by global references based on specific areas. This implies that the images to be processed contain textures of three main types to be identified: green plants, soil and sky if any. This paper proposes a new automatic approach for segmenting these main textures and also to refine the identification of sub-textures inside the main ones. Concerning the green identification, we propose a new approach that exploits the performance of existing strategies by combining them. The combination takes into account the relevance of the information provided by each strategy based on the intensity variability. This makes an important contribution. The combination of thresholding approaches, for segmenting the soil and the sky, makes the second contribution; finally the adjusting of the supervised fuzzy clustering approach for identifying sub-textures automatically, makes the third finding. The performance of the method allows to verify its viability for automatic tasks in agriculture based on image processing.

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Section: Revision Of Methodsmentioning

confidence: 99%

Automatic segmentation of relevant textures in agricultural images

Guijarro

Pajares

Riomoros

et al. 2011

Computers and Electronics in Agriculture

297

122

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“…Other approaches propose the use of the HSI color model combined with classification methods such as Bayes networks and clustering (Lee et al, 1996(Lee et al, ,1999Hemming and Rath, 2001;Blasco et al, 2002;Zheng et al, 2009). Segmentation can also be performed by selecting texture features based on their similarities with previous models encountered, stored in a database (Marti et al, 2001;Bosch et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Real-time image processing for crop/weed discrimination in maize fields

Burgos-Artizzu

Ribeiro

Guijarro

et al. 2011

Computers and Electronics in Agriculture

261

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Keywords:Computer vision Precision Agriculture Weed detection Real-time image processing This paper presents a computer vision system that successfully discriminates between weed patches and crop rows under uncontrolled lighting in real-time. The system consists of two independent subsystems, a fast image processing delivering results in real-time (Fast Image Processing, FIP), and a slower and more accurate processing (Robust Crop Row Detection, RCRD) that is used to correct the first subsystem's mistakes. This combination produces a system that achieves very good results under a wide variety of conditions. Tested on several maize videos taken of different fields and during different years, the system successfully detects an average of 95% of weeds and 80% of crops under different illumination, soil humidity and weed/crop growth conditions. Moreover, the system has been shown to produce acceptable results even under very difficult conditions, such as in the presence of dramatic sowing errors or abrupt camera movements. The computer vision system has been developed for integration into a treatment system because the ideal setup for any weed sprayer system would include a tool that could provide information on the weeds and crops present at each point in real-time, while the tractor mounting the spraying bar is moving.

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“…The processing time per image of the mean-shift based segmentation algorithm is about 24 times that of the ExG-and CIVE-based algorithm making it unsuitable for real-time applications (Zheng et al, 2009). The processing time of the proposed algorithm was slightly greater yet comparable to that of the vegetation index-based methods, as shown in Table 3, making it suitable for real-time applications.…”

Section: Test Datasetmentioning

confidence: 99%

“…Efficient and automatic segmentation of green vegetation from background is an important step not only for accurate recognition of crop rows, but also for many precision agriculture applications like weed detection for site-specific treatment. Despite recent developments, segmentation of green vegetation under uncontrolled illumination conditions in real time is still a major research challenge (Zheng et al, 2009). Significant research has been undertaken on the segmentation of green vegetation for crop row line tracking and identification of single plants (crops and weeds) for applications such as precision spraying.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, research has been carried out on developing complex, yet efficient, algorithms for vegetation segmentation. These techniques include unsupervised fuzzy clustering algorithms intensified by Zadeh's intensification technique (Meyer et al, 2004), mean-shift-based learning procedure (Zheng et al, 2009) and Environmentally Adaptive Segmentation Algorithm (EASA) (Tian & Slaughter, 1998). Simple, threshold-based methods cannot completely separate the vegetation from the background under naturally changing outdoor lighting conditions.…”

Section: Introductionmentioning

confidence: 99%

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Effective segmentation of green vegetation for resource-constrained real-time applications

Parvathi

Boigelot

Mercatoris

2015

Precision Agriculture '15

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This paper describes an improved algorithm for segmentation of green vegetation under uncontrolled illumination conditions and also suitable for resource-constrained real-time applications. The proposed algorithm uses a naïve Bayesian model to effectively combine various manually extracted features from two different color spaces namely RGB and HSV. The evaluation of 100 images indicated the better performance of the proposed algorithm than the vegetation index-based methods with comparable execution time. Moreover, the proposed algorithm performed better than the state-of-the-art EASA-based algorithms in terms of processing time and memory usage.

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Mean-shift-based color segmentation of images containing green vegetation

Cited by 142 publications

References 19 publications

Automatic segmentation of relevant textures in agricultural images

Automatic segmentation of relevant textures in agricultural images

Real-time image processing for crop/weed discrimination in maize fields

Effective segmentation of green vegetation for resource-constrained real-time applications

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