Improved discrimination among similar agricultural plots using red-and-green-based pseudo-colour imaging

Doi, Ryoichi

doi:10.1515/intag-2015-0086

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…Additionally, most of the grayscale images from the RmGB and R-mGB pseudocolor images had the heaviest loadings on the second or third principal component while none of them had the greatest loadings on the first principal component. This was fortunate because the grayscale images even more significantly added the unique information to the original ten and 15 grayscale images examined in previous studies [24, 25]. The second and third principal components were comparable in importance compared with the first principal component, and many grayscale images derived from the RmGB and R-mGB pseudocolor images formed significant regression models in another study [4].…”

Section: Resultsmentioning

confidence: 90%

“…When a user of test strips relies on multiple regression, the different patterns revealed by the principal components were effective in determining the chemical characteristics by combining the best grayscale images that carry the best combination of patterns in the grayscale intensity [4]. In finding differences among color profiles of image pixels representing agricultural plots [24] or tropical forest canopies [25], the 14 grayscale images from the RGyB and RG-yB pseudocolor images together with the RGB yellow hybrid image (Figure 1) generated minor but significant principal components. In this study, among the 15 grayscale images, nine had the highest loadings on the first principal component (Table 1).…”

Section: Resultsmentioning

confidence: 99%

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Maximizing the Accuracy of Continuous Quantification Measures Using Discrete PackTest Products with Deep Learning and Pseudocolor Imaging

Doi

2019

Journal of Analytical Methods in Chemistry

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Using the standard colors provided in the instructions, PackTest products can approximate and quickly estimate the chemical characteristics of liquid samples. The combination of PackTest products and deep learning was examined for its accuracy and precision in quantifying chemical oxygen demand, ammonium ion, and phosphate ion using a pseudocolor imaging method. Each PackTest product underwent reactions with standard solutions. The generated color was scanner-read. From the color image, ten grayscale images representing the intensity values of red, green, blue, cyan, magenta, yellow, key black, and L∗, and the values of a∗ and b∗ were generated. Using the grayscale images representing the red, green, and blue intensity values, 73 other grayscale images were generated. The grayscale intensity values were used to prepare datasets for the ten and 83 (=10 + 73) images. For both datasets, chemical oxygen demand quantification was successful, resulting in values of normalized mean absolute error of less than 0.4% and coefficients of determination that were greater than 0.9996. However, the quantification of ammonium and phosphate ions commonly provided false positive results for the standard solution that contained no ammonium ion/phosphate ion. For ammonium ion, multiple regression markedly improved the accuracy using the pseudocolor method. Phosphate ion quantification was also improved by avoiding the use of an estimated value for the reference solution that contained no phosphate ion. Real details of the measurements and the perspectives were discussed.

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Section: Resultsmentioning

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Maximizing the Accuracy of Continuous Quantification Measures Using Discrete PackTest Products with Deep Learning and Pseudocolor Imaging

Doi

2019

Journal of Analytical Methods in Chemistry

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“…Then, perform inverse Fourier transform, obtain three monochromatic images which represent different frequency components and perform further processing on these three images. Finally, add them to the red, green and blue display channels as three primary color components respectively and obtain a color image [11].…”

Section: Gray-scale-pseudo-color Transformation Methodsmentioning

confidence: 99%

An improved gray-scale transformation method for pseudo-color image enhancement

Gao¹,

Zeng²,

Chen³

2019

Computer Optics

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Image enhancement is a very important process of image preprocessing and it plays a critical role in the improvement of image quality and the follow-up image analysis, which makes the research of image enhancement algorithm a hot research field. Image enhancement not only needs to strengthen image determination and recognition, but also needs to avoid the consequential color distortion. Pseudo-color enhancement is the technique to map different gray scales of a black-andwhite image into a color image. As humans have extremely strong ability in distinguishing different colors visually and relatively weak capacity in discriminating gray scales, so, color the grayscale changes which cannot be differentiated by human eyes so that they can tell them apart. The mapping function in conventional gray-scale transform method is not working well in dark and low-contrast images. So, this paper comes up with an improved gray-scale transformation algorithm. This algorithm can achieve the enhancement, preserve the image colors, process dark and low-contrast images, reinforce the enhancement and improve the blocking effect. The experiment proves that the enhanced image obtained by the method of this paper can have improved average brightness, natural colors and more detail information and it has good application value.

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“…Mariotto et al’s report indicates that a larger number of bands results in a correspondingly larger number of images, providing more precise and accurate measures of changes in crop productivity. Because hyperspectral satellite imaging is associated with a larger number of significant dimensions as a result of the larger numbers of bands and images acquired for each scene, this technique enables higher R 2 values for crop productivity 13 . Similarly, spectrophotometers can precisely divide the light emission into narrow bands (eg, 600 nm).…”

Section: Introductionmentioning

confidence: 99%

Hue‐shifting for accurate and precise quantification of biochemical substances using diagnostic test strips

Doi

2021

Coloration Technology

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Some test strips have high feasibility for the quick and approximate quantification of the biochemical substances in samples. Recently, test strips became more precise and accurate by involving the quantitative observation of coloration and regression modelling. Their accuracy and precision can be significantly improved by hue‐shifting the colours of the original sample and those of the standards. The standard colour images included in the instructions of test strips for the detection of four urinary and two salivary biochemical substances were used. Images of glucose‐loaded test strips for urinary glucose detection were also prepared. Using the red‐green‐blue (RGB) colour image, the author prepared 11 hue‐shifted colour images by changing the hue of the original RGB colour image at intervals of 30°. From each of the 12 colour images, 10 greyscale images indicating values of the intensity of RGB, cyan, magenta, yellow, key black, L*, a* and b* were prepared. In regression modelling, the additional 110 greyscale images improved the values of the coefficient of determination and normalised mean absolute error in the quantification of protein, specific gravity, urobilinogen and glucose in urine, as well as of alcohol and nitric oxide in saliva. Further analyses showed that the hue‐shifting technique yielded new greyscale images that were more significantly related to the values of the biochemical variables in question than any previous greyscale images. This improvement was realised by the most significant linear measures for the description of concentrations of biochemical substances, and synergetic effects between significant greyscale images.

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Improved discrimination among similar agricultural plots using red-and-green-based pseudo-colour imaging

Cited by 6 publications

References 19 publications

Maximizing the Accuracy of Continuous Quantification Measures Using Discrete PackTest Products with Deep Learning and Pseudocolor Imaging

Maximizing the Accuracy of Continuous Quantification Measures Using Discrete PackTest Products with Deep Learning and Pseudocolor Imaging

An improved gray-scale transformation method for pseudo-color image enhancement

Hue‐shifting for accurate and precise quantification of biochemical substances using diagnostic test strips

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