Factors Affecting Color-Based Weed Detection

El-Faki, M. S.; Zhang, N.; Peterson, Dallas E.

doi:10.13031/2013.2968

Cited by 31 publications

(11 citation statements)

References 2 publications

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“…The RMSEs corresponding to L 3 and figure 8). The use of vegetation indices instead of the individual wavelength band can remove some of the variability in reflection caused by different light conditions (Tucker 1979, El-Faki et al 2000. The use of vegetation indices NDVI, DVI, SAVI, and VI opt decreased the noise on the measurements due to platform vibration and orientation (figures 3 and 8).…”

Section: Sensitivity Of VI Opt For Parameter Lmentioning

confidence: 99%

A linear model to predict with a multi‐spectral radiometer the amount of nitrogen in winter wheat

Reyniers

Walvoort

Baardemaaker

2006

International Journal of Remote Sensing

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The objective was to develop an optimal vegetation index (VI opt ) to predict with a multi-spectral radiometer nitrogen in wheat crop (kg[N] ha 21 ). Optimality means that nitrogen in the crop can be measured accurately in the field during the growing season. It also means that the measurements are stable under changing light conditions and vibrations of the measurement platform. Different fields, on which various nitrogen application rates and seeding densities were applied in experimental plots, were measured optically during the growing season. These measurements were performed over three years. Optical measurements on eight dates were related to calibration measurements of nitrogen in the crop (kg[N] ha 21 ) as measured in the laboratory. By making combinations of the wavelength bands, and whether or not the soil factor was taken into account, numerous vegetation indices (VIs) were examined for their accuracy in predicting nitrogen in wheat. The effect of changing light conditions in the field and vibrations of the measurement platform on the VIs were determined based on tests in the field. VI opt ((1 + L)*(R 2 NIR + 1)/(R red + L) with L50.45), the optimal vegetation index found, was best in predicting nitrogen in grain crop. The root mean squared error (RMSE), determined by means of cross-validation, was 16.7 kg[N] ha 21 . The RMSE was significantly lower compared to other frequently used VIs such as NDVI, RVI, DVI, and SAVI. The L-value can change between 0.16 and 1.6 without deteriorating the RMSE of prediction. Besides being the best predictor for nitrogen, VI opt had the advantage of being a stable vegetation index under circumstances of changing light conditions and platform vibrations. In addition, VI opt also had a simple structure of physically meaningful bands.

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Section: Sensitivity Of VI Opt For Parameter Lmentioning

confidence: 99%

A linear model to predict with a multi‐spectral radiometer the amount of nitrogen in winter wheat

Reyniers

Walvoort

Baardemaaker

2006

International Journal of Remote Sensing

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“…Woebbecke et al (1995) found that shifting from direct sun to shade influenced each of the three color channels almost equally, yet in their experiments, one can observe that some channels could be slightly more influenced than the others. El-Faki et al (2000a) also found out that the effect of variation in illumination on perceived colors was significant but relative. In their experiments they showed that the R, G and B plates slightly reduced their separation when illumination was increased.…”

Section: Discussionmentioning

confidence: 90%

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 are widely used in the segmentation of plant from imaging backgrounds [9][10][11][12][13] . Some color indices utilize only the red, green and blue spectral bands.…”

Section: ) Visible Light Rgb Image Segmentationmentioning

confidence: 99%

Study on spike detection of cereal plants

Cai

Berger

et al. 2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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The spike of a cereal plant is the grain-bearing organ whose physical properties are therefore critical components for plant yield. The ability to detect spikes from 2D images of cereals, such as wheat, provides vital information on tiller number and plants yield potential.We propose a novel spike detection method, which uses both RGB and fluorescence images. Firstly, an improved colour index method is used in the segmentation of plant feature from visible light RGB images, while threshold colour segmentation is applied to fluorescence images. Secondly, morphology algorithms are used to refine the initial segmentation results. Thirdly, a neural network-based method using Laws texture energy is performed for spike detection; connected and overlapping spikes are identified based on the average area and average perimeter of spikes and separated by a straight line through the center of the overlapping region. Finally, the spikes are extracted using area and height thresholds. Experimental results with two data sets have shown that the proposed method is simple, practical and can identify spikes with an accuracy of over 80%.

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Factors Affecting Color-Based Weed Detection

Cited by 31 publications

References 2 publications

A linear model to predict with a multi‐spectral radiometer the amount of nitrogen in winter wheat

A linear model to predict with a multi‐spectral radiometer the amount of nitrogen in winter wheat

Detecting creeping thistle in sugar beet fields using vegetation indices

Study on spike detection of cereal plants

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