Color Indices for Weed Identification Under Various Soil, Residue, and Lighting Conditions

Woebbecke, D. M.; Meyer, George E.; Bargen, K. Von; Mortensen, D. A.

doi:10.13031/2013.27838

Cited by 1,073 publications

(596 citation statements)

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“…Previously, Woebbecke et al (1995) investigated five true-colour indices derived from digitized images to separate living plant material from soil and crop residue. Woebbecke et al (1995) determined that an excess green index was most effective, and it has been used widely since (e.g. Meyer and Neto 2008).…”

Section: Image Analysismentioning

confidence: 99%

Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment

Bater

Coops

Wulder

et al. 2010

Environ Monit Assess

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Critical to habitat management is the understanding of not only the location of animal food resources, but also the timing of their availability. Grizzly bear (Ursus arctos) diets, for example, shift seasonally as different vegetation species enter key phenological phases. In this paper, we describe the use of a network of seven ground-based digital camera systems to monitor understorey and overstorey vegetation within species-specific regions of interest. Established across an elevation gradient in western Alberta, Canada, the cameras collected true-colour (RGB) images daily from 13 April 2009 to 27 October 2009. Fourth-order polynomials were fit to an RGB-derived index, which was then compared to field-based observations of phenological phases. Using linear regression to statistically relate the camera and field data, results indicated that 61% (r 2 = 0.61, df = 1, F = 14.3, p = 0.0043) of the variance observed in the field phenological phase data is captured by the cameras for the start of the growing season and 72% (r 2 = 0.72, df = 1, F = 23.09, p = 0.0009) of the variance in length of growing season. Based on the linear regression models, the mean absolute differences in residuals between predicted and observed start of growing season and length of growing season were 4 and 6 days, respectively. This work extends upon previous research by demonstrating that specific understorey and overstorey species can be targeted for phenological monitoring in a forested environment, using readily available digital camera technology and RGB-based vegetation indices.

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Section: Image Analysismentioning

confidence: 99%

Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment

Bater

Coops

Wulder

et al. 2010

Environ Monit Assess

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“…Images were recorded online and analyzed offline. The classification procedure of the images was: For image = 1 to N { Classify each grid cell and show decision } Vegetation was highlighted from the soil background in both the row and crop recognition images using the excessive green transformation defined by Woebbecke (1995b), Excessive Green, EG = 2ÁG-R-B. In the crop recognition images, a threshold on the excessive green values was set at 10 based on examining histograms of the data.…”

Section: Methodsmentioning

confidence: 99%

“…Subsequently, the crop recognition image was split into grid cells that represented about 100 mm 2 at the soil surface. If a grid cell contained vegetation, based on the excessive green value from Woebbecke (1995b), six features were determined for the specific grid cell. The features determined were: (1) distance to crop row, (2) mean red value, (3) mean green value, (4) mean EG value, (5) mean red-blue (RB) value and, (6) texture in terms of the length of edge segments.…”

Section: Methodsmentioning

confidence: 99%

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Adaptive detection of volunteer potato plants in sugar beet fields

2009

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Volunteer potato is an increasing problem in crop rotations where winter temperatures are often not cold enough to kill tubers leftover from harvest. Poor control, as a result of high labor demands, causes diseases like Phytophthora infestans to spread to neighboring fields. Therefore, automatic detection and removal of volunteer plants is required. In this research, an adaptive Bayesian classification method has been developed for classification of volunteer potato plants within a sugar beet crop. With use of ground truth images, the classification accuracy of the plants was determined. In the non-adaptive scheme, the classification accuracy was 84.6 and 34.9% for the constant and changing natural light conditions, respectively. In the adaptive scheme, the classification accuracy increased to 89.8 and 67.7% for the constant and changing natural light conditions, respectively. Crop row information was successfully used to train the adaptive classifier, without having to choose training data in advance.

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“…Excess Green (ExG) (Woebbecke et al, 1995) was used to separate vegetation from soil, followed by Otsu thresholding (Otsu, 1979) to produce a complete binary image segmenting the vegetation against the background as shown in Fig. 5.…”

Section: Image Preprocessing and Extraction Of Vegetation Indicesmentioning

confidence: 99%

Detecting creeping thistle in sugar beet fields using vegetation indices

Kazmi

García-Ruiz

Nielsen

et al. 2015

Computers and Electronics in Agriculture

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a b s t r a c tIn this article, we address the problem of thistle detection in sugar beet fields under natural, outdoor conditions. In our experiments, we used a commercial color camera and extracted vegetation indices from the images. A total of 474 field images of sugar beet and thistles were collected and divided into six different groups based on illumination, scale and age. The feature set was made up of 14 indices. Mahalanobis Distance (MD) and Linear Discriminant Analysis (LDA) were used to classify the species. Among the features, excess green (ExG), green minus blue (GB) and color index for vegetation extraction (CIVE) offered the highest average accuracy, above 90%. The feature set was reduced to four important indices following a PCA analysis, but the classification accuracy was similar to that obtained by only combining ExG and GB which was around 95%, still better than an individual index. Stepwise linear regression selected nine out of 14 features and offered the highest accuracy of 97%. The results of LDA and MD were fairly close, making them both equally preferable. Finally, the results were validated by annotating images containing both sugar beet and thistles using the trained classifiers. The validation experiments showed that sunlight followed by the size of the plant, which is related to its growth stage, are the two most important factors affecting the classification. In this study, the best results were achieved for images of young sugar beet (in the seventh week) under a shade.

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Color Indices for Weed Identification Under Various Soil, Residue, and Lighting Conditions

Cited by 1,073 publications

References 0 publications

Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment

Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment

Adaptive detection of volunteer potato plants in sugar beet fields

Detecting creeping thistle in sugar beet fields using vegetation indices

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