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The phenotypical manifestations of asthma among children are diverse and exhibit varying responses to therapeutic interventions. There is a need to develop objective biomarkers to improve the characterization of allergic and inflammatory responses relevant to asthma to predict therapeutic treatment responses. We have previously investigated histamine iontophoresis with laser Doppler flowmetry (HILD) as a potential surrogate biomarker that characterizes histamine response and may be utilized to guide the treatment of allergic and inflammatory disease. We have identified intra‐individual variability of HILD response type among children and adults with asthma and that HILD response type varied in association with racial classification. As laser Doppler flowimetry may be impacted by skin color, we aimed to further validate the HILD method by determining if skin color or tone is associated with observed HILD response type differences. We conducted an observational study utilizing quantification of skin color and tone obtained from photographs of the skin among participants during HILD assessments via the RGB color model. We compared RGB values across racial, ethnic, and HILD response type via the Kruskal‐Wallis test and calculated Kendall rank correlation coefficient to evaluate the relationship between RGB composite scores and HILD pharmacodynamic measures. We observed that RGB scores differed among racial groups and histamine response phenotypes (p < 0.05). However, there was a lack of correlation between the RGB composite score and HILD pharmacodynamic measures (r values 0.1, p > 0.05). These findings suggest that skin color may not impact HILD response variations, necessitating further research to understand previously observed differences across identified racial groups. [ABSTRACT FROM AUTHOR]

Crown diameter is one of the crucial indicators for evaluating the adaptability, growth quality, and ornamental value of garden chrysanthemums. To accurately obtain crown diameter, this study employed an unmanned aerial vehicle (UAV) equipped with a RGB camera to capture orthorectified canopy images of 64 varieties of garden chrysanthemums at different growth stages. Three methods, namely RGB color space, hue-saturation-value (HSV) color space, and the mask region-based convolutional neural network (Mask R-CNN), were employed to estimate the crown diameter of garden chrysanthemums. The results revealed that the Mask R-CNN exhibited the best performance in crown diameter estimation (sample number = 2409, R2 = 0.9629, RMSE = 2.2949 cm). Following closely, the HSV color space-based model exhibited strong performance (sample number = 2409, R2 = 0.9465, RMSE = 3.4073 cm). Both of the first two methods were efficient in estimating crown diameter throughout the entire growth stage. In contrast, the RGB color space-based model exhibited slightly lower performance (sample number = 1065, R2 = 0.9011, RMSE = 3.3418 cm) and was only applicable during periods when the entire plant was predominantly green. These findings provide theoretical and technical support for utilizing UAV-based imagery to estimate the crown diameter of garden chrysanthemums. [ABSTRACT FROM AUTHOR]

A robust, stability-indicating, and eco-friendly proton nuclear magnetic resonance (1H-qNMR) method was developed for the concurrent determination of three 1,4-benzodiazepines (BDZs), namely diazepam (DZP), alprazolam (ALP), and chlordiazepoxide (CDP) and their common impurity, synthesis precursor, and degradation product; 2-amino-5-chlorobenzophenone (ACB). In the present method, a novel approach was developed for composing a green and cost-efficient solvent system as an alternative to the common NMR organic solvents utilizing 0.3 M sodium dodecyl sulfate prepared in deuterated water. The conducted method is characterized by simplicity with no need for sample pretreatment or labeling. Phloroglucinol was used as an internal standard. The chosen signals for the determinations of ALP, CDP, DZP and ACB were at 2.35 ppm (singlet), 2.84 ppm (singlet), 3.11 ppm (singlet), and 6.90 ppm (doublet of doublet), respectively. The proposed method possessed linearity over the concentration range of 0.25–15.0 mg ml−1 for DZP, ALP, CDP and of 0.5–25.0 mg ml−1 for ACB with LOD values of 0.06, 0.03, 0.07 and 0.16 mg ml−1 respectively, and LOQ values of 0.18, 0.09, 0.21 and 0.49 mg ml−1, respectively. Accuracy of the method was evidenced by excellent recovery% (99.57–99.90%) and small standard deviation (≥ 1.10) for the three analyzed drugs. Intra- and inter-day precision were determined with coefficient of variation ranging from 0.12 to 1.14 and from 0.72 to 1.67, respectively. For the studied compounds, appraisal of the method greenness was achieved via four approaches: Analytical Eco-Scale, Green Analytical Procedure Index (GAPI), Analytical greenness metric (AGREE), and RGB Additive Color Model. The results proved that the proposed method has the privilege of being a green analytical method. [ABSTRACT FROM AUTHOR]

In the natural environment, complex and changeable meteorological factors can influence changes in the internal physiology and phenotype of crops. It is important to learn how to convert complex meteorological factor stimuli into plant perception phenotypes when analyzing the biological data obtained under the natural field condition. We restored the true gradation distribution of leaf color, which is also known as the skewed distribution of color scale, and obtained 20 multi-dimensional color gradation skewness-distribution (CGSD) parameters based on the leaf color skewness parameter system. Furthermore, we analyzed the correlation between the five corresponding meteorological factors and canopy CGSD parameters of peppers growing in a greenhouse and cabbages growing in an open air environment, built response model and inversion mode of leaf color to meteorological factors. Based on the analysis, we find a new method for correlating complex environmental problems with multi-dimensional parameters. This study provides a new idea for building a correlation model that uses leaf color as a bridge between meteorological factors and plants internal physiological state. [ABSTRACT FROM AUTHOR]

Online social media has an increasing influence on people's lives, providing tools for communication and self–representation. People's digital traces are gaining attention as a reflection of their personality traits, enhancing the personality computing tasks in various areas. This study aims at the identification of statistical associations between psychometric scores from three questionnaires—the Big Five Inventory, Plutchik's Lifestyle Index and the Eysenck Personality Questionnaire—and a set of graphical features of avatar images from the VK online social media that include the pixel characteristics from the HSV and RGB color models and the number of persons and faces depicted in an avatar. The problem is considered from the statistical point of view. The dependency between psychometric scores and the number of faces/persons is assessed with the Kruskal–Wallis test with Dunn test pairwise comparisons. The color-pixel characteristics that are associated with the psychometric scores are selected with several fits of the regularized regression with L 2 and MCP penalties. The data for the study were collected via a specially developed application for the online social media platform VK. The results of the analysis support existing research on how colors express personality and discover certain color-pixel image characteristics that could be used for personality computing models. [ABSTRACT FROM AUTHOR]

Inconsistent lighting phenomena in digital images, such as underexposure and overexposure, pose challenges in computer vision. Many studies have developed to address these issues. However, most of these techniques cannot remedy both exposure problems simultaneously. Meanwhile, existing methods that claim to be capable of handling these cases have not yielded optimal results, especially for images with blur and noise distortions. Therefore, this study proposes a system to improve underexposed and overexposed photos, consisting of two different residual attention convolution networks with the CIELab color space as the input. The first model working on the L-channel (luminance) is responsible for recovering degraded image illumination by using residual memory block networks with self-attention layers. The next model based on dense residual attention networks aims to restore degraded image colors using ab-channels (chromatic). A properly exposed image is produced by fusing the output of these models and converting them to RGB color space. Experiments on degraded synthetic images from two public datasets and one real-life exposure dataset demonstrate that the proposed system outperforms the state-of-the-art algorithms in optimal illumination and color correction outcomes for underexposed and overexposed images. [ABSTRACT FROM AUTHOR]

In contrast, rectangles of the trichromat of color make no stripe since the newly combined illuminators reveal white pixel boundaries for the rectangles (Fig. The image seems counterintuitive; that is, the rectangles of the trichromat of light reveal clear stripes of their CMY components on their edges, while the rectangles of the trichromat of color do not. The color rectangles of the trichromat of color have no separation, while rectangles of the trichromat of light have clear stripes of their CMY color components on their edges. The color rectangles of the trichromat of light have no separation,[13] while rectangles of the trichromat of color have clear stripes of their RGB color components on their edges. [Extracted from the article]

The current study strives to provide a haptic attribute space where texture surfaces are located based on their haptic attributes. The main aim of the haptic attribute space is to come up with a standardized model for representing and identifying haptic textures analogous to the RGB model for colors. To this end, a four dimensional haptic attribute space is established by conducting a psychophysical experiment where human participants rate 100 real-life texture surfaces according to their haptic attributes. The four dimensions of the haptic attribute space are rough-smooth, flat-bumpy, sticky-slippery, and hard-soft. The generalization and scalability of the haptic attribute space is achieved by training a 1D-CNN model for predicting attributes of haptic textures. The 1D-CNN is trained using the attribute data from psychophysical experiments and image features collected from the images of real textures. The prediction power granted by the 1D-CNN renders scalability to the haptic attribute space. The prediction accuracy of the proposed 1D-CNN model is compared against other machine learning and deep learning algorithms. The results show that the proposed method outperforms the other models on MAE and RMSE metrics. [ABSTRACT FROM AUTHOR]

Background: Malachite green (MG) and brilliant green (BG) are two synthetic triphenylmethane dyes with applications in the textile and aquaculture industries. They are considered to be environmental contaminants due to their carcinogenic and mutagenic properties. Both dyes have the same bluish-green color in aqueous solutions. Objective: The aim of this study is to develop a colorimetric analysis as a very simple and cost-effective method to determine the residues of MG and BG simultaneously in aqueous industrial samples. Method: This method is based on the alterations in red, blue, green (RGB) color histograms of the dyes in the presence and absence of Triton X-100 micelle. The images of the samples were taken by a digital camera and converted to the RGB color system using MATLAB software. Partial least-squares regression as a powerful chemometrics tool was used for multivariate calibrations and quantitative measurements. The performance of the proposed method was compared with a simple spectrophotometric method as a reference. Results: Relative errors of prediction for colorimetric and spectrophotometric analysis, respectively, in micellar media were 6.56 and 4.61% for MG and 6.38 and 5.24% for BG. The shortest linear ranges for colorimetric and spectrophotometric analysis, respectively, in micellar media were 0.1–10 and 0.5–5 mg/L for MG and 0.1–15 and 0.5–6 mg/L for BG. The recovery percentages obtained from the analysis of the dyes in real samples of fish-pond water and textile wastewater ranged between 91 and 107%. Conclusions: The good correlation between the results of the colorimetric analysis and the spectrophotometric analysis indicates the reliability of the proposed colorimetric method. Also, the results of the relative recovery study showed insignificant matrix effect. Highlights: This study demonstrates the ability of the colorimetric analysis coupled with chemometrics tools for simultaneous determination of the analytes even with nearly identical colors. [ABSTRACT FROM AUTHOR]

Adequate management of agricultural crops requires, among other things, accessible and sufficiently accurate methods for assessing plant nutrition and crop vegetation status and for agricultural production estimation. Sustainable technologies are based on correct decisions, prompt interventions and appropriate works, and correct information in real time, and the obtaining information methods can be simple, accessible, and appropriate in relation to different user categories (e.g., farmers, researchers, decision makers). This study used mineral fertilization (NPK), with 11 experimental variants, to ensure a controlled differentiated nutrition of the wheat plants, "Alex" cultivar. Regression analysis was used to obtain models in estimating wheat production, by methods based on: (a) NPK fertilizers applied (F) in the 11 experimental variants; (b) physiological indices (PI), represented by the chlorophyll content (Chl), and plant nutrition status on the experimental variants, in terms of macroelement content in the leaves, evaluated by foliar diagnosis (Nfd, Pfd, Kfd); (c) imaging analysis (IA) based on digital images of the wheat experimental variants, and calculated indices. A set of models was obtained, with different precision levels and statistical safety: R2 = 0.763, p = 0.013 for the model based on applied fertilizers (NPKF); R2 = 0.883, p < 0.01 for the model based on foliar diagnosis (NPKfd); R2 = 0.857, p < 0.01 for the model based on chlorophyll content (Chl); R2 = 0.975, p < 0.01 for the model based on normalized rgb color parameters (RGB color system); R2 = 0.925, p < 0.01 for the model based on the DGCI calculated index. The model based on applied fertilizers (F model) was tested in relation to wheat production data, for a period of six years, communicated by other studies. Fit degree analysis between predicted yield based on the F model and real yield (six-year average) was confirmed by R2 = 0.717, compared to R2 = 0.763 for the F model in this study. The models obtained in this study, related to the "Alex" wheat cultivar, can be used for other studies, but with a certain margin of error, given the coefficient values, specific to the obtained equations. The approach concept, methods, and models presented can be opportunities for other studies to facilitate their comparative analysis, their adaptation, and/or development in the form of new models that are useful in different studies, research, or agricultural practices, for their integration into crop management strategies. [ABSTRACT FROM AUTHOR]

Color correction is an image-altering technique that modifies image color in such a way that it matches a reference image. Many approaches have already been proposed by various researchers; however, those models have been unable to reduce color errors between two images, which results in inefficiency and poor-quality images. This research paper presents an effective and improved color correction model wherein alternate least square (ALS) and root polynomial (RP) are used together. The main objective of the proposed model is to reduce the error between a reference image and a target image to enhance the image quality and make them look realistic. To achieve this objective, the proposed model used the Amsterdam library of object images which contains a picture of single objects captured under various illumination angles and colors. The main contribution of this paper is a hybrid ALS + RP color correction technique, implemented on the dataset image that fixes its color as per the reference image and enhances its quality. The target image is then converted into three color models, i.e., LAB, LUV, and RGB into XYZ format. Finally, the color difference between a reference image and a target image is observed by calculating values for parameters like Mean, median, 95% quantile, and maximum error. The effectiveness of the suggested hybrid color correction approach is assessed and validated in MATLAB software for each color model. Through extensive experiments, it is observed that the proposed hybrid model yields the least errors for the RGB color model. This is followed up by LUV and then LAB, to prove its supremacy over other models. [ABSTRACT FROM AUTHOR]

The article focuses on the emergence of a new cultivar of Yellow-horn, called 'Yan Rui', which exhibits strong adaptability to cold and drought, making it suitable for cultivation in northern China. Topics include the origin and characteristics of 'Yan Rui', its dissemination, and cultivation recommendations, highlighting its ornamental, ecological, and agricultural value.

The development of a portable device created by 3D printing for colorimetric and fluorometric measurements is an efficient tool for analytical applications in situ or in the laboratory presenting a wide field of applications in the environmental and food field. This device uses a light-emitting diode (LED) as radiation source and a webcam as a detector. Digital images obtained by the interaction between the radiation source and the sample were analyzed using a programming language developed in Matlab (Mathworks Inc., Natick, MA, USA), which builds the calibration curves in real-time using the RGB colour model. In addition, the entire system is connected to a notebook which serves as an LED and detector power supply without the need for any additional power source. The proposed device was used for the determination in situ of norfloxacin, allura red, and quinine in water and beverages samples, respectively. For the validation of the developed system, the results obtained were compared with a conventional spectrophotometer and spectrofluorometer respectively with a t-test at a 95% confidence level, which provides satisfactory precision and accuracy values. [ABSTRACT FROM AUTHOR]

Soil color is commonly used as an indicator to classify soil and identify its properties. However, color-based soil assessments are susceptible to variations in light conditions and the subjectivity of visual evaluations. This study proposes a novel method of calibrating digital images of soil, regardless of lighting conditions, to ensure accurate identification. Two different color space models, RGB and CIELAB, were assessed in terms of their potential utility in calibrating changes to soil color in digital images. The latter system was determined to be suitable, as a result of its ability to accurately reflect illuminance and color temperature. Linear regression equations relating soil color and light conditions were developed based on digital images of four different types of soil samples, each photographed under 15 different light conditions. The proposed method can be applied to calibrate variations in the soil color obtained by digital images, thus allowing for more standardized, objective, and accurate classification and evaluation of soil based on its color. [ABSTRACT FROM AUTHOR]

Methods of molecular genetic analysis based on the colorimetric biochip technology have shown their effectiveness in identifying antibiotic resistance genes in bacteria. For the quantitative determination of nucleic acids, a comparative study of methods for converting digital color images of biochips into monochrome black-and-white versions using RGB and CMYK color models has been carried out. A 19-mer single-stranded oligonucleotide and two model mRNAs corresponding to the genes of two types of clinically relevant beta-lactamases (CTX-M and NDM) were studied as objects. The widest range of staining intensity and the best analytical characteristics for the determination of all types of studied nucleic acids were obtained using the red channel of the RGB color model. The detection limits were 0.10 ± 0.02 pmol/μL for the 19-mer oligonucleotide, and 3.0 ± 0.2 and 8.0 ± 0.6 amol/μL for mRNA of beta-lactamases CTX-M-116 and NDM-1, respectively. The developed method can be used for the quantification of genes responsible for the multiple resistance of bacteria to antimicrobial drugs. [ABSTRACT FROM AUTHOR]

Background: Indocyanine green (ICG) near-infrared fluorescence cholangiography (NIRF-C) is widely used to visualize the biliary tract during laparoscopic cholecystectomy (LC). However, the ICG dose and its dosing time vary in the literature so there is not a standard ICG protocol. The objectives of this descriptive prospective study were to demonstrate that NIRF-C at a very low dose of ICG provides good visualization of the extrahepatic biliary tree while avoiding hepatic hyperluminescence and to assess the surgeon-perceived benefit. Furthermore, another additional aim was quantifying the amount of ICG dye in the liver tissue and biliary tract through a green colour intensity (GCI) analysis according to red green blue (RGB) color model and correlating it to surgeon-perceived benefit. Method: Forty-four patients were scheduled for LC. We recorded demographics, surgical indication, intraoperative details, adverse reactions to ICG, hepatic hyperluminescence, visualization of the cystic duct (CD), the common bile duct (CBD) and the cystic duct–bile duct junction (CDBDJ) before and after dissection of Calot's triangle, operation time, surgical complications and subjective surgeon data. For all procedures, a unique dose of 0.25 mg of ICG was administered intravenously during the anaesthetic induction. ICG NIRF-C was performed using the overlay mode of the VISERA ELITE II Surgical Endoscope in all surgeries. Video recordings of all 44 LC were reviewed. Using a color analysis software, the GCI of CBD versus adjacent liver tissue was calculated using RGB color model. Results: ICG NIRF-C was performed in all 44 cases. The mean operation time was 45 ± 15 min. There were no bile duct injuries (BDIs) or allergic reactions to ICG. The postoperative course was uneventful in all of cases. The mean postoperative hospital stay was 28 ± 4 h. ICG NIRF-C identified the CBD in 100% of the patients, the CD in 71% and the CDBDJ in 84%, with a surgeon satisfaction of 4/5 or 5/5 in almost 90% of surgeries based on a visual analogue scale (VAS). No statistically significant differences were found in the visualization of the biliary structures after the dissection of Calot's triangle in obese patients or with gallbladder inflammation. Furthermore, 25% of patients with a BMI ≥ 30, 27% of patients with a Nassar grade ≥ 3 and 21% of patients with gallbladder inflammation had a VAS score 5/5 compared to 6% of patients with a BMI < 30 (p = 0.215), 6% of patients with a Nassar grade < 3 (p = 0.083) and none of the patients without gallbladder inflammation (p = 0.037). Measured pixel GCI of CBD was higher than adjacent hepatic tissue for all cases regardless of the degree of gallbladder inflammation, the Nassar scale grades or the patient's BMI (p < 0.0001). In addition, a significant correlation was observed between surgeon-perceived benefit and the amount of ICG dye into the CBD according the RGB color model (p < 0.0001). Conclusion: ICG NIRF-C at a very low dose of ICG (0.25 mg of ICG 20 min before surgery) enables the real-time identification of biliary ducts, thereby avoiding the hepatic hyperluminescence even in cases of obese patients or those with gallbladder inflammation. [ABSTRACT FROM AUTHOR]

In order to improve the recall and precision of image retrieval, a visual retrieval method of digital media image features based on active noise is proposed. In this paper, Canny algorithm is used to detect the edge of the image to get the feature information of the edge of the image. The RGB color space model is used to decompose the color information of the image, and the color characteristic information of the image is obtained. Extracting image features to retain the useful information contained in the image as much as possible; In order to facilitate the visual retrieval of image features, reduce the retrieval complexity and further fuse image features, FPCA and ReliefF algorithms are used to reduce the dimensionality of image features, and the active noise control method is used to sharpen the image. After processing the results, a digital media image feature visual retrieval platform is established to realize the visual retrieval of digital media image features. Experimental results show that the proposed method has a high accuracy of over 80% and a high recall rate of 95.2%. [ABSTRACT FROM AUTHOR]

Deep learning has gradually been one of the most important technologies in the field of agriculture in recent years. However, the problems of labeling quality and cost of training samples for supervised deep learning have become the bottleneck of restricting the development of technology. In order to reduce the cost of deep model training and ensure that the model can have high image segmentation accuracy, in this study, a model named Bounding-box Mask Deep Convolutional Neural Network (BM-DCNN) was proposed to realize automatic training and segmentation for maize plant. First of all, using DJI’s Genie 4-RTK drone to collect top images of maize seedlings. The flight uses an automatic take-off planned route, and the entire route covers the entire test field. Second of all, using the open source labeling tool called Labelme to label top images of maize seedlings. The top images of the original maize seedling plants need to be labeled twice. In this study, we used bounding boxes as the basic shapes for weakly supervised labels, and pixels within the bounding boxes area were marked as foreground(i.e. the possible effective pixels of a maize plant). Pixels outside the bounding boxes were marked as background. Finally, the information of bounding boxes was used to generate primary pseudo-labels on the images, and the RGB color model of the images was converted to the HSV(Hue-Saturation-Value) color model, and the full connection condition random field(DenceCRF) was used to eliminate the influence of plant shadow and the image noise on the pseudo-labels accuracy in the images. The pseudo-labels were trained on the optimized YoLact model instead of the ground truth labels. The optimized model can be used for the instance segmentation of the plants at the maize seedling stage. We designed an experiment for verification and testing of BM-DCNN. By comparing the similarity between pseudo-labels mask and ground truth, it found that the mean intersection over union (mIoU) was 81.83% and mean cosine similarity (mcos(ɑ)) was 86.14%, which was higher than the accuracy of pseudo-labels generated by Grabcut(the mIoU was 40.49% and mean cosine similarity was 61.84%). For the maize seedling image (top view), the time cost of three manual annotation methods was calculated, with bounding box labels of 2.5 min/sheet, scirbbles labels of 15.8 min/sheet, and pixel-level labels of 32.4 min/sheet. Considering that the ground truth labels had an error in the handing of maize plant details, the pseudo-labels at the accuracy can be used for deep convolutional neural network training. By comparing the accuracy of instance segmentation between BM-DCNN and fully supervised instance segmentation model, when the IoU value of the BM-DCNN was greater than 0.7(AP70), the instance segmentation accuracy corresponding to the BM-DCNN model was higher than that of the supervised model. The average accuracy of the two backbone networks of the BM-DCNN model were 67.57% and 75.37%, respectively, which were close to the supervised instance segmentation results under the same conditions (67.95% and 78.52%, respectively), and the higher average accuracy can reach 99.44% of the supervised segmentation results. Therefore, For the instance segmentation task of the maize seedling plants images(top view), the instance segmentation effect of BM-DCNN can almost achieve the segmentation effect of the supervised instance segmentation model under the same conditions. It can be seen that in the large-area operation scenario of the UAV, it was feasible to use the bounding box labels of the images to replace the ground truth labels to complete the training of deep learning model, which greatly reduced the time cost of manual labeling of the samples, and provided theoretical support for the rapid realization of the application scenarios, such as the number of plants at the seedling stage of maize and the calculation of canopy coverage. [ABSTRACT FROM AUTHOR]

Medical optical imaging, with the aid of the "terahertz tomography", is a novel medical imaging technique based on the electromagnetic waves. Such advanced imaging techniques strive for the detailed theoretical and computational analysis for better verification and validation. Two important aspects, the analytic approach for the understanding of the Schrodinger transforms and machine learning approaches for the understanding of the medical images segmentation, are presented in this manuscript. While developing an AI algorithm for complex datasets, the computational speed and accuracy cannot be overlooked. With the passage of time, machine learning approaches have been further modified using the Bayesian, genetic and quantum approaches. These strategies have boosted the efficiency of the machine learning, and specifically the deep learning tools, by taking into account the probabilistic, evolutionary and quantum qubits hypothesis and operations, respectively. The current research encompasses the detailed analysis of image segmentation algorithms based on the evolutionary approach. The image segmentation algorithm that converts the color model from RGB to HSI and the image segmentation algorithm that uses the clustering technique are discussed in detail, and further extensions of these genetic algorithms to quantum algorithms are proposed. Based on the genetic algorithm, the optimal selection of parameters is realized so as to achieve a better segmentation effect. [ABSTRACT FROM AUTHOR]

Point clouds are now a standard way of describing objects in many engineering disciplines, whether they are man-made objects such as structures, buildings, or various types of structures. Commonly used methods of acquiring such data include ground, UAV, or even aerial photogrammetry, followed by terrestrial, UAV, and aerial scanning. After measurement (by the scanner) or calculation (from photogrammetry), the point cloud goes through extensive processing that essentially transforms the unordered mass of points into a usable data set. One of the important steps is removing points representing obstructing objects and features, including vegetation in particular. Here, many filtering methods based on different principles are available and suitable for application to different scenes. This paper presents a new method of filtering point clouds based on the visible spectrum color principle using vegetation indexes determined from RGB system colors only. Since each sensor has to some extent, an individual interpretation of the colors, it cannot be assumed to determine specific boundaries of what is and is no longer vegetation. Therefore, it was proposed to use means clustering to simplify the operator's work. The method was also designed in such a way that the entire evaluation could be implemented in the freely available CloudCompare software. The procedure was tested on three different sites with different terrain and vegetation characteristics showing, which demonstrated the applicability of this method to data where the color information (green) uniquely identifies vegetation. The selected vegetation filters ExG, ExR, ExB, and ExGr were tested, where ExG was the best. Kmeans clustering helps an operator to distinguish more easily between vegetation and the rest of the point cloud without compromising the quality of the result. The method is practically implementable using the freely downloadable and usable CloudCompare software. [ABSTRACT FROM AUTHOR]

Agriculture is the primary source in providing food for the entire nation. Consequently, agriculture is the fundamental origin of food supply. The contributions of agriculture include increase in employment opportunity and economy of the nation. According to IBEF, in India 58% of entire population depends on agriculture as their main occupation. Currently 81.1% of the total agricultural production is produced by livestock farmers. There will be 50% of loss in yield because of pest and disease. The disease in the plant excitants farmers to use unsuitable pesticide which causes unfavorable consequences. This may lead to the reduction in soil and food quality. Besides it has an adverse impact on human life. Nevertheless, farmers are heedless of these effects. The diseases which are naturally created will cause a serious impact on yields also it will bring down the quality of the food and soil. The symptoms which can cause the less yield are infinitesimal and because of the less human vision potentiality it is difficult to recognize the disease. Plant diseases will require genuine identification and proper categorization of the crops. The developed advanced methodology will identify the diseases, percentage of spread area, pesticide name with the quantity of the pesticide require to heal particular disease using image processing technique. [ABSTRACT FROM AUTHOR]

Rapid methods for the determination of the antibacterial compounds amoxicillin and cephalexin from the β-lactam group based on visual-colorimetric and colorimetric evaluation using a smartphone camera were developed. The reaction of ninhydrin with the amino groups of antibiotics was chosen as an indicator. The interaction of immobilized ninhydrin with gentamicin on the surface of a solid matrix was evaluated using diffuse reflectance spectroscopy. The stability and the effects of temperature and heating time of the test agents on the course of the reaction were studied. Color scales for the visual-colorimetric determination of amoxicillin and cephalexin were obtained. Linear dependences of the intensities of the Green channel of the RGB color model on the logarithm of antibiotic concentration were plotted. The procedures developed were applied to the analysis of drug samples. [ABSTRACT FROM AUTHOR]

[Display omitted] • A digital image colorimetric method to determine the acidity of oils was proposed. • Excellent correlation (R2 = 0.98) with standard method results was obtained. • Chemicals were reduced by about 90% compared to the titration method. • Precision and accuracy were in accordance with validation guidelines. The standard method used to quantify free acidity (FA) in vegetable oil is neutralization titration, which requires many toxic chemicals and depends on an analyst's experience in detecting endpoints. Here, a digital image colorimetry (DIC) method using a smartphone camera was developed to measure FA in vegetable oils. A cupric acetate solution was used to produce the colorimetric reaction. The coloured solutions were imaged, and R values (from the RGB colour system) were calibrated against the respective FAs in the standards. The FA values of the samples were determined by standard addition calibration. These results were compared to measurements of FA obtained by the standard titrimetric method. An excellent correlation was obtained, with an R2 of 0.98 and a mean absolute error of 0.06%. The chemicals needed for analysis were reduced by approximately 90%. Thus, DIC is a less subjective and more economical method for determining FA in vegetable oils. [ABSTRACT FROM AUTHOR]

• Morphological processing and feature value extraction of the collected dried fruit images of Z. armatum DC. • Convert RGB color model to HIS color model to obtain color characteristic values. • BP neural network model was constructed to identify and classify the dried fruit of Z. armatum DC. In order to achieve rapid and accurate grading of Zanthoxylum armatum DC. dried fruit, this study utilized image processing technology combined with BP neural networks for the dried fruit of Z. armatum identification to enhance the market value of Z. armatum and their products. The results showed that preprocessing the images with a yellow background yielded the best results, effectively distinguishing the external contours and internal images of Z. armatum. The extracted image of the dried fruit can represent information about its peel, with R, G, and B values being the largest at 69.3285, 65.6432, and 31.2561, respectively. Among the seven drying methods, the average G value was highest at 71.0560 under the conditions of blowing at 42℃, whereas it was lowest at 47.3840 under sunlight drying. Moreover, R, G, B, H, S, and I represent the input layer nodes of the BP neural network. The change in the number of hidden layers did not correlate with the classification results. The classification accuracy of the BP neural network for Z. armatum classification under all dry conditions was 79.53 %-98.04 %, indicating high reliability. The proposed method can provide a theoretical basis for future research on Z. armatum classification. A schematic diagram of color grading of green Sichuan pepper(Zanthoxylum armatum DC.)dried fruit. [Display omitted] [ABSTRACT FROM AUTHOR]

Salvia miltiorrhiza Bunge is an outcross-pollinated plant with diverse flower colors, ranging from white to purple. To clarify the genetic basis of S. miltiorrhiza flower color, we crossed white-flowered S. miltiorrhiza f. alba with dark violet-flowered S. miltiorrhiza , and selfed F1 to obtain an F2 population. The RGB color system was used to describe the flower color of the parents, F1 progeny, and F2 individuals. Afterward, we used genotyping-by-sequencing technology to construct a high-density linkage map of S. miltiorrhiza based on the F2 population. Finally, the linkage map was used to locate the QTLs of the genes that control flower color in S. miltiorrhiza. Through measurement and cluster analysis of the R, G, and B values of flowers from the parents, F1, and F2 individuals, it was found that the purple flower color of S. miltiorrhiza is a quantitative trait controlled by 2 loci of major genes. The genetic map contained 605 SNPs with a total length of 738.3 cM in 8 linkage groups (LGs), and the average distance between 2 markers was 1.22 cM. Based on the constructed genetic map and the flower R, G, B, and R+G+B values, 2 QTLs were detected for flower color, located on LG4 and LG5. The results of this study lay the foundation for cloning genes that control flower color and studying the molecular mechanism of flower color regulation in S. miltiorrhiza. [ABSTRACT FROM AUTHOR]

A simple analytical approach based on capillary zone electrophoresis with ultraviolet detection and repeated sample injection strategy (applied in a hydrodynamically closed separation system for the first time) was developed for the determination of ibuprofen (IBU) in commercially available pharmaceutical preparations. The proposed method was characterized by significantly increased sample throughput and favorable validation parameters, highly demanded in routine quality control laboratories. The limit of detection was predicted at the concentration level of 0.31 µg/mL. Intra-day precision expressed as the relative standard deviation of IBU concentration ranged from 1.9 to 5.6%, and corresponding intra-day accuracy expressed as the relative error was in the interval of 87.1–106.5%. Inter-day precision was in the range of 2.6–15.0%, and inter-day accuracy was 94.9–102.7%. The developed method was able to quantify IBU in complex pharmaceutical matrices represented by commercially available tablets and oral suspension. The determined contents of IBU in the tested dosage forms were in good agreement with the manufacturer's declaration. The analytical performance of the developed method was evaluated according to the innovative RGB Additive Color Model strategy. It was demonstrated that the proposed method is characterized by very good analytical performance parameters, safety and eco-friendliness, and practical effectiveness. [ABSTRACT FROM AUTHOR]

A semiconductor chip usually has thousands test parameters in order to guaranteed its quality. Hence, a batch of chips' test data set include thousands of float data. The primary goal of dealing with this test data is to obtain the fault parameter distribution and judge the chip's quality. It is a challenge due to the large scale and complex relationship of the test data set. This paper presents a novel method to analyze the test data set by meshing the quality theory and scientific data visualization. First, transfer the test data set to a quality classifier matrix Q: a series of quality region is defined based on quality theory, which is the baseline to classify the test data set into different group and mark them with various number. Second, form a quality-spectrum: define a color rule based on the RGB color model and color the quality classifier matrix Q. Hence chip's quality distribution could be observed through the quality-spectrum. Furthermore, by analyzing the quality-spectrum, the chip's quality could be quantitative and fault diagnose has a data basic. One case is included to illustrate appropriateness of the proposed method. [ABSTRACT FROM AUTHOR]

A detection of fake photos is a serious concern since general users can easily create with mobile apps and computer software. In this paper, a novel method that can detect fake photos accurately is proposed. RGB color model permutations are considered and non-decimated shift-invariant wavelet transform is applied. The proposed method extracts features using the Markov process and texture operator based on co-occurrence in both the spatial and frequency domains. The feature vector dimension is reduced by using an infinite feature selection algorithm and feature selection provides quality features to improve a detection accuracy and reduce a classification model training time. The experimental analysis is performed on four photo forgery datasets and demonstrated the accuracy of the proposed scheme is outstanding for both types of forgery, splicing and copy-move when compared with previous forgery detection schemes. [ABSTRACT FROM AUTHOR]

This study is aimed to develop a computer program to analyze the color properties of raster images of histological microspecimens used in pathological anatomy and forensic medicine. When developing a computer program, we used the system for building client applications – Windows Presentation Foundation (WPF). The system allows you to create applications with visually attractive user interaction capabilities. The programming language is C#, as well as basic graphics capabilities of the .NET Framework system are used. To speed up the display we applied double buffering. In the course of the research, the authors developed a modern computer program «Analysis of color properties of raster images». This program allows you to analyze the color of each individual pixel of a photograph in RGB and Lab color models, comparing the colors and brightness of individual pixels, selecting groups of points and determining statistical characteristics of them. Characteristic points are well distinguishable in the photographs, studied with the program «Analysis of color properties of raster images». This makes it possible to select and automate these properties, using computer recognition algorithms, completely removing human factor’s influence on the analysis results. The computer program «Analysis of color properties of raster images» is of significant scientific and practical interest for specialists both in the field of morphology (pathologists, forensic experts, etc.), and in the field of other biomedical disciplines. [ABSTRACT FROM AUTHOR]

The use of the RGB color model in colorimetric chemical sensing via imaging techniques is widely prevalent in the literature. However, the lack of specificity in the selection of RGB color space during capture and analysis presents a significant challenge in creating standardised methods for this field and possible discrepancies. In this study, we conducted a comprehensive comparison and contrast of a total of 68 RGB color spaces to evaluate their respective impacts on colorimetric bio/chemical sensing. We explore the impact of dynamic range, sensitivity, and limit of detection, and show that the lack of specificity in RGB color space selection can significantly impact colorimetric chemical sensing by 42–77%. We also explore the impact of underlying RGB comparisons and demonstrate a further 18.3% discrepancy between RGB color spaces. By emphasising the importance of proper RGB color space selection and handling, our findings contribute to a better understanding of this critical area and present valuable opportunities for future research. We further provide valuable insights for creating standardised methods in this field, which can be utilised to avoid discrepancies and ensure accurate and reliable analysis in colorimetric bio/chemical sensing. [Display omitted] • Unspecified RGB color spaces can compromise chemical sensing via imaging techniques. • Sensory characteristics significantly affected by RGB color space selection (42–77%). • Conversion between RGB spaces can lead to further discrepancies (18%). • 68 RGB color spaces compared - proper selection is critical for accurate analysis. • Proposed method herein can contribute to standardised methods/analysis in the field. [ABSTRACT FROM AUTHOR]

As an endogenous species and a milk adulterant, urea determination is important for quality control in dairy industry. This work proposes a novel, simple, and cost-effective method to determine urea by the analysis of digital images and videos. Urea hydrolysis under urease catalysis releases ammonia and the increase in pH makes feasible the photometric measurements based on the dissociation of the phenol red acid-base indicator. Digital images were acquired by a smartphone camera under controlled conditions and intensities of the reflected radiation were converted to the RGB color system using a free app (PhotoMetrix®). Digital videos were exploited for procedure optimization and evaluation of matrix effects from RGB values obtained with ImageJ® software. A linear response was achieved within 2.0–25.0 mg L-1 urea (R2 =0.996), with a coefficient of variation (n = 10) of 1.6%, and limits of detection (95% confidence level) and quantification of 0.5 and 1.5 mg L-1, respectively. Recoveries from 95% to 112% were estimated from samples spiked with urea and results for commercial milk samples achieved by the standard additions method agreed with the reference values at the 95% confidence level. The procedure is applicable to whole, semi-skim, and skim milk either processed using pasteurization or under ultra-heat treatment. [Display omitted] • Exploitation of enzymatic hydrolysis for urea determination in milk. • Digital videos for kinetic monitoring and detection of matrix effects. • Application to different kinds of milk without pretreatment. • An environmentally friendly approach with microliter amounts of sample and reagents. • Accurate results achieved by the standard additions method. [ABSTRACT FROM AUTHOR]

Despite structural colors based on metasurfaces drawing enormous attentions due to the high resolutions and superior durability, few researches focus on transmission colors in RGB model with high saturation because high‐monochromaticity transmission peak is hard to be realized. Herein, a strategy of high‐performance transmission structural colors in RGB model with high saturation, full hue, high efficiency, easy manufacturability, and polarization independence by means of Al‐Si3N4 nanoblocks deposited on glass substrate is proposed. The coupling between Wood's anomaly and Mie lattice resonance is capable of introducing magnetic‐dipole and electric‐quadrupole resonances, creating enhanced resonant peak with full width of half maximum (FWHM) of 50 nm and efficiency of over 70% in transmission mode. High‐saturation transmission colors occupying 150% standard RGB (sRGB) space can be obtained for the first time by tailoring the geometric parameters. Besides, it is also demonstrated that the hybrid nanostructures can realize full‐hue colors with constant resolutions, which is significant for imaging applications. The designed method has potential for further extending the applications of transmission colors. [ABSTRACT FROM AUTHOR]

In this paper, a theoretical framework for Optical Wireless Communication using RGB color model with computer monitor display and digital camera is proposed. The motivation is to find a cheaper alternative to physical network switches and wired and optical cables in communication networks that can be leveraged for computer clusters, thus reducing time and costs for purchase, setup, maintenance, power consumption and cooling. Also, providing better data transfer rates, scalability and band width conservation. The framework is distinguished from related work by the use of RGB for data encoding at various bit-depths. Greater data transfer rates than existing Optical Wireless Communication systems are possible. A computer monitor displays a grid of changing colors controlled by a transmitting host. The color in a grid cell represents one or more data bits as per the RGB codes used. The RGB codes can be computed by logically partitioning the RGB cube, a geometrical representation of the RGB model. A camera on a receiving host, samples the colors to obtain the data bits. Mathematical expressions are derived to compute the RGB codes and mappings to data bits. The upper bound of the data rate is derived and compared with the well-known Shannon communication theorem. The framework is extended to include an array of digital cameras for parallel data copy in a computer cluster and the data rate is determined. Theoretically, the data rate can increase beyond the Gigabit/second realm, and into Terabit/second realm. [ABSTRACT FROM AUTHOR]

Our computational developments and analyses on experimental images are designed to evaluate the effectiveness of chemical spraying via unmanned aerial vehicle (UAV). Our evaluations are in accord with the two perspectives of color-complexity: color variety within a color system and color distributional geometry on an image. First, by working within RGB and HSV color systems, we develop a new color-identification algorithm relying on highly associative relations among three color-coordinates to lead us to exhaustively identify all targeted color-pixels. A color-dot is then identified as one isolated network of connected color-pixel. All identified color-dots vary in shapes and sizes within each image. Such a pixel-based computing algorithm is shown robustly and efficiently accommodating heterogeneity due to shaded regions and lighting conditions. Secondly, all color-dots with varying sizes are categorized into three categories. Since the number of small color-dot is rather large, we spatially divide the entire image into a 2D lattice of rectangular. As such, each rectangle becomes a collective of color-dots of various sizes and is classified with respect to its color-dots intensity. We progressively construct a series of minimum spanning trees (MST) as multiscale 2D distributional spatial geometries in a decreasing-intensity fashion. We extract the distributions of distances among connected rectangle-nodes in the observed MST and simulated MSTs generated under the spatial uniformness assumption. We devise a new algorithm for testing 2D spatial uniformness based on a Hierarchical clustering tree upon all involving MSTs. This new tree-based p-value evaluation has the capacity to become exact. [ABSTRACT FROM AUTHOR]

With the increasing maturity of digital imaging technology and the increasing popularity of high resolution camera equipment, the advantages of high resolution and low cost have prompted the use of digital imaging technology to conduct more qualitative and quantitative descriptions of phenotypic traits for plant appearance. The RGB model is the most commonly used color representation for digital images. In order to explore the feasibility of using color gradation distribution parameters of the RGB model in soybean yield prediction, and to verify the universality of the method in different fertilizer operations and varieties, two soybean varieties, Qujing and Xudou 18, were selected to design field fissure experiments with different densities and nitrogen fertilizer levels in this study. Digital cameras were carried by Unmanned Aerial Vehicle (UAV) to collect soybean canopy digital images during three important reproductive growth stages. The results showed that the cumulative distribution of canopy color gradation of soybean at the florescence, pod-setting and grain-filling stages, all conformed to the skewed distribution, and a total of 20 Color Gradation Skewness-Distribution (CGSD) parameters were obtained by skew analysis. These parameters simultaneously described the shade and distribution of the canopy leaf color. The 20 CGSD parameters were significantly different among the florescence, pod-setting and grain-filling stages. And the variation trend of color depth parameters (mean, median, and mode) was opposite to that of the distribution parameters (skewness and kurtosis). The prediction model of soybean yield by using prediction model multiple stepwise regression method was constructed based on CGSD parameters with P value of 0.012. The model had high estimation accuracy in both the modeling group and the verification groups. The prediction accuracy of the model in modeling group reached 91.30% on average; the average prediction accuracy of 18 plots in the nitrogen operation research validation group was 87.33%. Although the prediction accuracy of the validation group for different varieties was lower than that of the modeling group and the validation group for nitrogen fertilizer operation research, it was also close to 80%. In conclusion, the RGB color model based on skewness distribution provided detailed soybean canopy image information, and the canopy color information quantitatively described systematically from the degree of depth, distribution bias and uniformity. And thus the yield prediction model based on CGSD parameters had high prediction accuracy, which can be widely used to predict yield of soybean grown in different production conditions. At the same time, the use of UAV and digital cameras improves the efficiency of image acquisition, while reduces the cost of image acquisition, which is more conducive to the popularization and application of this method. [ABSTRACT FROM AUTHOR]

Color analysis is a critical approach used to analyze the quality properties of fruits, particularly in tracing the ripening stage through the peel color of the fruits. In this study, the color features of natural and untreated tropical fruit juice images (ie, B10 Averrhoa carambola L. and Sala mango) were correlated with the pH of the juice samples. The CIELAB color characteristics of the fruits showed that the mango juice had darker features than the carambola juice. The cause may be due to the higher soluble solid content and denser yellow and red pigments in the mango juice. For carambola juice, multiple linear regression (MLR) performed on red‐blue (RB) components generated the best prediction of pH with the highest coefficient of determination and lowest root mean square of error. For mango juice, MLR performed on normalized red‐green‐blue (RGB) components generated the best prediction of pH. [ABSTRACT FROM AUTHOR]

Sclerotium of ergot is hard fungal body which contain toxic alkaloids to human and livestock. These alkaloids include ergotamine, ergovaline, ergocornine, ergocryptine, and ergocristine. Consumption of ergot can lead to human harmful health effects. These include constriction of blood flow to extremities, gangrene, hallucinations, muscle spasms and vomiting. Livestock fed ergot sclerotia can develop gangrene symptoms of ears, hooves and tails. The alkaloids can also cause abortions and reduce mammary gland development. The study was carried out through 2019 at the agriculture engineering department, faculty of agriculture Tanta University, Egypt. To prevent the harmful health effects in humans and livestock’s using separating methods which depend on physical properties and image analysis software for different varieties of wheat imported to Egypt which contaminated with ergot fungi sclerotia. The average dimensions of ergot fungi sclerotia were ranged according imported place the length ranged from 2.32 to 22.51 mm. width from 0.12 to 2.91 mm. thickness from 0.12 to 2.21 mm. volume from 0.0511 to 65.133 mm³, geometric mean diameter from 0.46 to 4.98 mm, the arithmetic mean diameter from 1.7 to 9.10 mm, and sphericity, from 7.9 to 55.6%. Also surface area ranged from 0.66 to 78.02 mm² . On the other hand, the average dimensions of the different varieties of Russian Ukrainian and French wheat were: length 5.24, 5.24, 5.17 mm, width 1.92, 1.92, 2.35 mm, thickness 162, 161, 186 mm, volume 8.81, 8.75, 12.1 mm³, arithmetic mean diameter 2.52, 2.52, 2.82 mm², geometric mean diameter 2.92, 2.92, 3.13 mm², sphericity 48.2, 48.1, 57.8%, aspect ratio 36.7, 36.7, 45.9, and surface area 20.3, 20.2, 25.2 mm² respectively. These results revealed that the differences between the physical properties of wheat varieties and ergot sclerotia is not strong spicily for length, width and thickness this case led to obstructing separation processes. [ABSTRACT FROM AUTHOR]

Zenopontonia soror (Nobili, 1904) (Caridea: Palaemonidae) is a shrimp symbiont of several sea star species that exhibits homochromy with distinct mimetic coloration according to host species. To date, populations of Z. soror have been studied extensively in the Indo-Pacific and Gulf of Panama; however, those present in the Gulf of California (Mexico) have not been evaluated. Here we conduct the first evaluation of the diversity of chromotypes present in Z. soror populations in Bahía de La Paz (Baja California Sur, Mexico), and experimentally tested how long shrimp take to archieve mimicry post sea star switch. We hypothesized that Z. soror individuals rapidly change coloration (minutes to few hours) throughout their ontogeny to mimic the coloration of host species to decrease risk of predation. This hypothesis was tested by translocating Z. soror individuals to a different sea star species with different coloration and background in the field and under laboratory conditions for three consecutive days. At least 18 chromotypes of Z. soror were identified among 386 observed specimens based on the RGB color system; and colors were independent of body size. Each chromotype holds different relative abundance in the population. Coloration was not distinctive of each individual, while color intensity increased along ontogeny development from semi-transparent (juveniles) to solid coloration (adult phase). Dynamic contraction or dispersion of the chromatophores (pigment migration) caused changes in coloration to resemble coloration of the basibiont sea star. Full mimicry of new host was achieved within 36 h in field and laboratory conditions. Zenopontonia soror individuals were observed to mimic the white spines of the sea star host Acanthaster solaris by exhibiting a white dorsal band, while others selected color-concealing hosts to stay cryptic for a short time. We conclude that Z. soror dynamically shifts coloration as an adaptation to match the symbiont shrimp–sea star host coloration to minimize or prevent predation, but individuals are vulnerable to be detected by visual predators during the color transition (<36 h period). The combined use of the Hogben and Slome index and the RGB color system from digital images provide the most precise method to assign intensity and color in epibiont-host organisms to understand the complex process of homochromy. • Zenopontonia soror has least 18 chromotypes changing independently of body size. • Each chromotype holds distinct relative abundance in the population. • Color intensity increased to ontogenetic development from transparent (juveniles) to solid coloration (adults). • Dynamic contraction or dispersion of the chromatophores cause change in coloration to resemble coloration of the basibiont. • Full homocromy of new host was achieved within 36 h in field and laboratory conditions. [ABSTRACT FROM AUTHOR]

The present study shows the use of an Artificial Neural Network (ANN) as a novel strategy to study the relationship between the physicochemical profile and the color acquired by Tequila 100% agave during its maturation process. As a first step, the physicochemical characterization of different classes of Tequila (n = 12) was carried out using gas chromatography, Ultraviolet-Visible (UV-Vis) spectroscopy, and RGB analysis. The results show a linear increase between the aging time and the concentration of higher alcohols, esters, and aldehydes in the beverage. Likewise, UV-Vis spectroscopy show a linear increase in the intensity at 365 nm, attributable to the total flavonoid index, with aging time. Digital image processing based on the RGB color model was used to determine the Color Intensity and Tonality. The above information was fed to an ANN as inputs with the aim of classifying Tequila 100% agave samples into their respective classes based on their maturation time. With a confusion matrix, a correct classification was done in all the samples having an excellent correlation with the measurements performed in the analytical techniques with Tequila 100% agave maturation process. The results show the use of tools based on artificial intelligence as an auxiliary strategy for evaluating quality control in aged beverages. [Display omitted] Volatile compounds concentration was studied to understand maturation process. ANN permits to classify Tequila 100% agave samples in its classes. The results are useful as an auxiliary tool in the quality control of the beverage. [ABSTRACT FROM AUTHOR]

Nitrogen management in tea (Camellia sinensis L.) fields has deeply depended on manual work for a long term due to low-cost labor. Because of the decreasing population, costs of labor have been rising. Computer technology recently has made great success in many fields, and intelligent tea fields expert system will be updated again. Nitrogen management with computer technology instead of manual work is an inevitable attempt. In this study, UAVs were used to take photos of tea plants, meanwhile, the elder tea leaves and tea shoots were collected to determine the nitrogen content. According to the HSV color system, pixels of background, elder tea leaves, and tea shoots were screened respectively. The experimental strategy I and II then were established. In experimental strategy I, pixel-class information of elder tea leaves and tea shoots were used to calculate weighted average values on RGB and HSV color systems, respectively. The values then were respectively combined with the nitrogen content of elder tea leaves and tea shoots to build datasets. In experimental strategy II, after suppressing the background, not only the weighted average values on RGB and HSV color systems in the rest each photo but also the proportion of elder tea leaves and tea shoots were computed. The proportion values were respectively multiplied by the nitrogen content to obtain a new weighted nitrogen content, and then with weighted RGB and HSV values together formed datasets. The ordinary least squares (OLS), extreme gradient boosting (XGBoost), long short-term memory (RNN-LSTM), convolution neural network (CNN), and residual network (ResNet) models were designed and carried out on predicting nitrogen content. Generally, the performance of HSV datasets was better than that of RGB datasets. ResNet and CNN models performed best in experimental strategy I; the ResNet model had the best results in experimental strategy II. Both experimental strategies performed well when predicting nitrogen content in spring tea; as for summer tea, experimental strategy II had better performance. This method had the advantages of convenience and accuracy and could elevate intelligent tea field management. As a part of precision agriculture, it could improve intelligent tea fields expert system, and further deal with challenges caused by the labor population decrease. [ABSTRACT FROM AUTHOR]

The aim of the research was to analyze the relationship of color indicators of soil layers with the parameters obtained by ground-penetrating radar (GPR) profiling. Such parameters were the data of the spectral Fourier transform of the amplitude of the reflected pulse in the subsurface horizons of the soils of the Kamennaya Steppe area. GPR profiling was carried out by a Loza-V geophysical device. The diffraction points of reflected pulse amplitude corresponding to local soil horizons and groundwater surface were determined. The variability in the thickness of horizons and layers was calculated through the modules of amplitude maxima taking into account the conductivity of the horizon (dielectric permittivity and the rate of signal passage in a given medium). The procedure of verification of differentiation of sounding points was based on their comparison with real soil profiles and patterns. Statistical processing included the calculation of mean values, standard deviations, and frequencies of signal amplitude distribution; the use of the principal component method; and cluster analysis. Two polynomial models of connection of changes in parameters of the spectral Fourier transform of electromagnetic pulses in subsurface medium of chernozems with attributive parameters of formation of spectral pure colors of dominant wavelength (λi) were constructed. The indicative parameters included the conditional coefficient of pure color (Kλi), which corresponds to the addition functions in the tri-color colorimetric system MKO XYZ 1931, and its saturation, which is completely determined by the properties of the reflection coefficient (R) in the spectral range of λ 580–720 nm (red band). The calculated parameters of soil chromaticity obtained on the basis of these models were the prerogative for constructing the RGB model of soil layers and its visualization in graphic editors. [ABSTRACT FROM AUTHOR]

This paper presents a new and simple algorithm to eliminate haze from a single image using YIQ colour space and Dark Channel Prior (DCP). The suggested method consists of two parts. The first one is enhancement by adjusting contrast and lightness using contrast-limited adaptive histogram equalization and gamma transform. The second part involves applying DCP on a hazy image using the RGB colour model to achieve a haze-free image. The resulting image is then converted into YIQ, and the chrominance components (I and Q) are extracted and combined with the enhanced luminance component (Y) to achieve an enhancement haze-free image. The suggested method is applied to many outdoor images, and the quality of the resulting images is compared with that of images from other methods, by using several measures of quality. Analysing the results explain that the suggested method has a higher quality value than other methods. [ABSTRACT FROM AUTHOR]

The increasing production and extensive use of biodiesel in the latest years call for the development of fast and cost-effective procedures for point-of-care analysis. One of the main quality parameters is the biodiesel content in diesel blends, which needs to conform to regional legislations. In this work, a microfluidic device exploiting chemical derivatization of alkyl esters and detection by smartphone-based digital-image colorimetry was developed. It was designed to ensure proper experimental conditions for chemical derivatization, including reagent release, and photometric measurements. Analytes reacted with alkaline hydroxylamine yielding the corresponding alkyl hydroxamates, measured as colored Fe(III) complexes. Analytical response was based on the measurement of the G (green) channel from RGB color system. By taking methyl linoleate as a model compound, a linear response was obtained from 0.1% to 0.6%(v/v) (Analytical signal = 69.6 +2.1 C, r = 0.999), coefficient of variation (n = 10) of 4.0% and limit of detection (99.7% confidence) of 0.04%(v/v). Procedure consumes 1.2 µL of sample, 230 µg of hydroxylamine, 480 µg of NaOH, 14 µg of Fe(III) and equivalent to 1.2 µL of 69%(v/v) HNO 3. Accurate results were achieved in relation to the MIR reference method, with agreement at the 95% confidence level. • Microfluidic device with smartphone-based digital image for point-of-care analysis. • Practical and environmental friendly determination of biodiesel in diesel blends. • Cost-effective and easily constructed microfluidic device. • Exploitation of pNIPAN polymer as a reagent compartment. • Application to biodiesel produced from different raw materials. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel approach for determination of protein in food. • A green and cost-effective approach for ammonia distillation and detection. • Microdistillation combined with smartphone-based digital image colorimetry. • Simple, practical, and environmentally friendly strategy. • Application to determination of protein in animal and vegetal samples. Because of their nutritional importance and industrial interest, determination of proteins is usual in food quality control. The Kjeldahl method, the most usual for protein determination and a reference to other indirect methods, is time-consuming and generates significant amounts of waste. Aiming to circumvent these drawbacks, this work proposes a simple, cost-effective, and more environmental friendly procedure based on microdistillation of the ammonium from Kjeldahl digests, absorption on an acid-base indicator solution, and digital-image colorimetry for protein determination in food. Microdistillation was carried out in a simple and inexpensive lab-made apparatus. The digital images were acquired by a smartphone camera under controlled illumination and the intensity of the reflected radiation was converted to the RGB color system using a free app (PhotoMetrix®). Measurements were based on the color change of phenol red from yellow to pink and the G channel (corresponding to the complementary color of the dissociated indicator) was taken as analytical response. A linear response was achieved within 5.0–50.0 mg/L ammonium, equivalent to 0.003–0.03 % protein (r = 0.996), as confirmed by the lack-of-fit test at the 95 % confidence level. Coefficient of variation (n = 10) and limit of detection (99.7 % confidence level) were estimated at 2.3 % and 2 mg kg−1 protein, respectively. A sample throughput of 12 h−1 was achieved with simultaneous sample processing. Protein amounts determined in foods of animal (milk) and vegetal (beans and lentils) origin yielded results in agreement with the micro-Kjeldahl reference procedure at the 95 % confidence level. [ABSTRACT FROM AUTHOR]

In this study, a simple and sensitive digital image-based flow-batch analyzer (DIB-FBA) was developed for the chemical speciation of iron in tomato samples. The Fe(II) determination is based on the Fe(II)/1,10-phenanthroline reaction. Total iron was determined by reducing all Fe(III) to Fe(II) with ascorbic acid, and Fe(III) was calculated by subtracting Fe(II) content from the total iron content. Digital images were obtained using a webcam, reducing costs and simplifying the instrumentation. RGB data was extracted using software written in ActionScript 3.0, and the B component was employed to build the analytical curve. Samples of different tomato types and certified reference material were successfully analyzed using the DIB-FBA, which presented satisfactory performance parameters such as working range (0.1–1.0 mg L−1), LOD (2.33 μg L–1), LOQ (7.72 μg L–1), RSD (0.05% to 0.45%), analytical frequency (64 h–1), sample (150 µL) and waste (1.9 mL) volume. Furthermore, according to paired t-test at a 95% confidence level, no significant differences between the results from the DIB-FBA and those from the reference methods were found. The advantageous combination of digital image and flow automation provided by the DIB-FBA can be further explored for the speciation of other metals in diverse food samples. • A digital image-based flow-batch analyzer for iron speciation in tomato was presented. • The digital images were processed using the RGB color system. • The proposed method provided satisfactory analytical features. • Improved performance of proposed method when compared with previous studies. • The proposed method is an excellent automation tool for rapid iron speciation in tomato. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low segmentation accuracy and high computation time by applying existing segmentation methods for underwater color images, this paper has proposed an underwater color image segmentation method via RGB color channel fusion. Based on thresholding segmentation methods to conduct fast segmentation, the proposed method relies on dynamic estimation of the optimal weights for RGB channel fusion to obtain the grayscale image with high foreground-background contrast and reaches high segmentation accuracy. To verify the segmentation accuracy of the proposed method, the authors have conducted various underwater comparative experiments. The experimental results demonstrate that the proposed method is robust to illumination, and it is superior to existing methods in terms of both segmentation accuracy and computation time. Moreover, a segmentation technique is proposed for image sequences for real-time autonomous underwater vehicle operations. [ABSTRACT FROM AUTHOR]

A novel colorimetric assay was proposed to predict microbiological contamination of milk from pH measurements. It was based on the adsorption of the acid−base indicator phenol red to milk proteins, with the color of the suspension monitored by smartphone-based photometry through the RGB color system. Sample throughput of 30 measurements h−1, relative standard deviation of 2.0 % (n = 20), cost-effectiveness, low waste generation (570 µL per determination), and a broad analytical range (5.4 ≤ pH ≤ 7.2) were achieved. The procedure was applied for pH determination in raw and processed milk samples and the results agreed with near-infrared spectroscopy and potentiometric reference procedures at the 95 % confidence level. Relative errors were lower than 2.0 % with recoveries from 97 % to 106 %. A screening approach based on the analytical responses at G and B channels yielded the correct classification of samples masked with ammonium chloride, sodium hydroxide, urea, or formaldehyde, as well as acid and diluted milk, even when the pH of non-conforming samples was adjusted to the expected values (pH from 6.60 to 6.80). This advantage in relation to potentiometric pH measurements demonstrates the potentiality of the proposed approach for evaluation of microbiological contamination of milk. [Display omitted] • Green analytical alternative to predict milk microbiological contamination. • Accurate determination of milk pH. • Direct photometric measurements using a smartphone camera. • Screening for substances masking milk pH and microbiological contamination. • Approach suitable for both raw and processed cow milk. [ABSTRACT FROM AUTHOR]