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Approaches for remote sensing can be used to estimate the influence of changes in environmental conditions on terrestrial plants, providing timely protection of their growth, development, and productivity. Different optical methods, including the informative multispectral and hyperspectral imaging of reflected light, can be used for plant remote sensing; however, multispectral and hyperspectral cameras are technically complex and have a high cost. RGB imaging based on the analysis of color images of plants is definitely simpler and more accessible, but using this tool for remote sensing plant characteristics under changeable environmental conditions requires the development of methods to increase its informativity. Our review focused on using RGB imaging for remote sensing the characteristics of terrestrial plants. In this review, we considered different color models, methods of exclusion of background in color images of plant canopies, and various color indices and their relations to characteristics of plants, using regression models, texture analysis, and machine learning for the estimation of these characteristics based on color images, and some approaches to provide transformation of simple color images to hyperspectral and multispectral images. As a whole, our review shows that RGB imaging can be an effective tool for estimating plant characteristics; however, further development of methods to analyze color images of plants is necessary. [ABSTRACT FROM AUTHOR]

Variations in illumination and image background present challenges for using UAV RGB imagery. Existing studies often overlook these issues, especially in rice. To separately evaluate the impacts of illumination variation and image background on rice LAI assessment, this study utilized Retinex correction and image segmentation to eliminate illumination variations and background effects, and then analyzed the changes in color indices and their relationship with LAI before and after implementing these methods separately. The results indicated that both Retinex correction and image segmentation significantly enhanced the correlation between color indices and LAI at different growth stages as well as the accuracy of constructing a multivariate linear regression model separately. Our analysis confirmed the significance of accounting for variation in illumination and rice field backgrounds in LAI analysis when using UAV RGB images. Illumination variation and image background elements significantly degrade the accuracy of LAI estimation. [ABSTRACT FROM AUTHOR]

In this research, three different types of hydrocolloids (chitosan,tragacanth, hydroxy propyl methyl cellulose) 2 % on the basis of flour weight and three different kinds of emulsifiers (lecithin, glycerol mono stearate, propylene glycol) 1 % on the basis of flour weight, were added in different proportions to sponge cake's formulation in order to assay quality indicators of cake. Bulk density, true density, aw (water activity), moisture, amylose content, texture analysis, microbial growth and colour (hue, chroma) were used as quality indices during cake's shelf life period (12 weeks). Colour, aw and firmness did not significantly affect by any of hydrocolloids and emulsifiers and they affected only by the storage time. HPMC and lecithin had the most effect on decreasing true density and bulk density respectively. Results indicated that chitosan was the most effective hydrocolloid for retention of moisture in cakes. The amylose content increased by increasing lecithin during the six weeks and PG had significant effect in last three weeks of storage time. Lecithin also significantly affected the Fmax, so the cake's with high levels of lecithin in formulation had tender crust, and hardness decreased during the first six weeks of shelf life. No microbial growth was reported in addition of different proportions of hydrocolloids and emulsifiers. Except of lecithin, two other emulsifiers had no significant effect on Chroma index in comparison with control cakes. [ABSTRACT FROM AUTHOR]

The goal of this work was to examine the effects of sonication time, edible coating concentration (with guar gum), and °Brix (sucrose solution) on the osmotic dehydration (OD) parameters (mass reduction, water loss, soluble solids gain, and rehydration ratio) and the appearance properties (color indices and surface area) of quince slices using a response surface methodology (RSM) approach based on the central composite design (CCD), for the optimization of the process. The process parameters, sonication treatment time (5–10 min; 40 kHz and 150 W), edible coating concentration using guar gum (0.05%–0.15%, w/w), and osmotic concentration using sucrose solution (20%–50%, w/w), were investigated and optimized for OD of quince slices. After each OD process, the quince slices were dehydrated in an oven at 70°C for 240 min. Results demonstrated a good correlation between empirical data with the linear model. Using the optimization method, optimum input operating conditions were determined to be a sonication time of 5 min, guar gum concentration of 0.05%, and sucrose concentration of 37.19°Brix. At this optimum point, the OD process of quince slices reached the optimal mass reduction (17.74%), water loss (25.77%), soluble solids gain (8.03%), rehydration ratio (206.19%), lightness (77.6), redness (0.60), yellowness (34.84), total color change (ΔE) (8.92), and area changes (7.59%). [ABSTRACT FROM AUTHOR]

Variations in illumination and image background present challenges for using UAV RGB imagery. Existing studies often overlook these issues, especially in rice. To separately evaluate the impacts of illumination variation and image background on rice LAI assessment, this study utilized Retinex correction and image segmentation to eliminate illumination variations and background effects, and then analyzed the changes in color indices and their relationship with LAI before and after implementing these methods separately. The results indicated that both Retinex correction and image segmentation significantly enhanced the correlation between color indices and LAI at different growth stages as well as the accuracy of constructing a multivariate linear regression model separately. Our analysis confirmed the significance of accounting for variation in illumination and rice field backgrounds in LAI analysis when using UAV RGB images. Illumination variation and image background elements significantly degrade the accuracy of LAI estimation.

Approaches for remote sensing can be used to estimate the influence of changes in environmental conditions on terrestrial plants, providing timely protection of their growth, development, and productivity. Different optical methods, including the informative multispectral and hyperspectral imaging of reflected light, can be used for plant remote sensing; however, multispectral and hyperspectral cameras are technically complex and have a high cost. RGB imaging based on the analysis of color images of plants is definitely simpler and more accessible, but using this tool for remote sensing plant characteristics under changeable environmental conditions requires the development of methods to increase its informativity. Our review focused on using RGB imaging for remote sensing the characteristics of terrestrial plants. In this review, we considered different color models, methods of exclusion of background in color images of plant canopies, and various color indices and their relations to characteristics of plants, using regression models, texture analysis, and machine learning for the estimation of these characteristics based on color images, and some approaches to provide transformation of simple color images to hyperspectral and multispectral images. As a whole, our review shows that RGB imaging can be an effective tool for estimating plant characteristics; however, further development of methods to analyze color images of plants is necessary.

Aboveground biomass (AGB) is an important basis for wheat yield formation. It is useful to timely collect the AGB data to monitor wheat growth and to build high-yielding wheat groups. However, as traditional AGB data acquisition relies on destructive sampling, it is difficult to adapt to the modernization of agriculture, and the estimation accuracy of spectral data alone is low and cannot solve the problem of index saturation at later stages. In this study, an unmanned aerial vehicle (UAV) with an RGB camera and the real-time kinematic (RTK) was used to obtain imagery data and elevation data at the same time during the critical fertility period of wheat. The cumulative percentile and the mean value methods were then used to extract the wheat plant height (PH), and the color indices (CIS) and PH were combined to invert the AGB of wheat using parametric and non-parametric models. The results showed that the accuracy of the model improved with the addition of elevation data, and the model with the highest accuracy of multi-fertility period estimation was PLSR (PH + CIS), with R2, RMSE and NRMSE of 0.81, 1248.48 kg/ha and 21.77%, respectively. Compared to the parametric models, the non-parametric models incorporating PH and CIS greatly improved the prediction of AGB during critical fertility periods in wheat. The inclusion of elevation data therefore greatly improves the accuracy of AGB prediction in wheat compared to traditional spectral prediction models. The fusion of UAV-based elevation data and image information provides a new technical tool for multi-season wheat AGB monitoring. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is an important basis for wheat yield formation. It is useful to timely collect the AGB data to monitor wheat growth and to build high-yielding wheat groups. However, as traditional AGB data acquisition relies on destructive sampling, it is difficult to adapt to the modernization of agriculture, and the estimation accuracy of spectral data alone is low and cannot solve the problem of index saturation at later stages. In this study, an unmanned aerial vehicle (UAV) with an RGB camera and the real-time kinematic (RTK) was used to obtain imagery data and elevation data at the same time during the critical fertility period of wheat. The cumulative percentile and the mean value methods were then used to extract the wheat plant height (PH), and the color indices (CIS) and PH were combined to invert the AGB of wheat using parametric and non-parametric models. The results showed that the accuracy of the model improved with the addition of elevation data, and the model with the highest accuracy of multi-fertility period estimation was PLSR (PH + CIS), with R2, RMSE and NRMSE of 0.81, 1248.48 kg/ha and 21.77%, respectively. Compared to the parametric models, the non-parametric models incorporating PH and CIS greatly improved the prediction of AGB during critical fertility periods in wheat. The inclusion of elevation data therefore greatly improves the accuracy of AGB prediction in wheat compared to traditional spectral prediction models. The fusion of UAV-based elevation data and image information provides a new technical tool for multi-season wheat AGB monitoring.

Marmalade is a processed product of some plant organs such as fruit, flower, fruit peel and tuber in which the plant parts used are small and well homogenized. Sugar is used as a sweetener in the preparation of marmalade. In this study, the use of date syrup as a substitute for sucrose used in marmalade formulation was studied. The constrained mixture design was used to achieve the optimal formulation and the effect of sugar substitution with date palm syrup on properties of marmalade was studied. The ingredients used in the formulation were plum paste (35-65%), sugar syrup (0-65%) and date syrup (0-65%). Ten formulas were produced based on the constrained mixture design and their physical, chemical and sensorial properties were investigated. The results showed that plum paste had the greatest effect on increasing the viscosity and total acidity of marmalade, while total soluble solids decreased with increasing the amount of plum paste in the formulation. The L* index of the samples decreased with increasing date syrup and plum paste and the redness of marmalade increased with increasing plum paste. Sensory evaluation of the produced formulations showed that the increase in plum paste increased the consistency, spreadability, color and overall acceptance, while the sweetness and taste and flavor were more affected by the increase in date syrup. Optimal formulation was obtained in the ratios of 58% plum paste, 12% sugar syrup and 30% date syrup and in the optimal formulation the total acidity, total phenolic compounds, and viscosity of marmalade at 20 °C were equal to 2.07 g citric acid/100 g, 112.4 mg gallic acid/100 g, and 3961 cp, respectively. [ABSTRACT FROM AUTHOR]

The physicochemical stability of six bread formulations with different tilapia flour (BTF) levels (0%, 2.5%, 5%, 10%, 15%, and 20%) in substitution to wheat flour was investigated regarding moisture content, water activity (aw), pH, instrumental color parameters, texture profile, lipid and protein oxidation on days 0, 2, 4, 6 and 8 at 25°C. BTF10%, BTF15%, and BTF20% displayed lower (P

Drying is one of the most important post-harvesting processes with the aim of increasing the storage time. The present study investigates and compares color changes and shrinkage of red beetroot thin layers in four different drying methods (hot air flow, microwave, vacuum and freeze drying). The process of changing these two parameters was simulated by fitting to different models. The experiments were carried out in hot air and vacuum air dryers at three temperature levels (50, 70 and 90 °C), in microwave dryer at a power level of 180, 360 and 600 W, and in a freeze dryer at -50 °C.The results showed that the changes in the brightness index (L) and yellowness (b) relative to the time were increasing and the trend of changes in the red index (a) was decreasing. The slightest changes in the parameters of brightness, redness and yellowness occurred in freeze drying, microwave and vacuum, respectively. The overall results showed that the highest amount of color change (ΔE) was 1.59 in drying by microwave and at 180 W, and the lowest drying rate was 0.76 in freeze drying. Also, shrinkage changes in sugar beet samples showed that the use of freeze dryer reduced the amount of shrinkage by 10-25% compared to the other three methods of drying. Also, modeling of color parameters and shrinkage showed that exponential models have higher ability to predict the process of color variation and shrinkage. [ABSTRACT FROM AUTHOR]

The design of proxy techniques is an innovative tool to monitor the potentially toxic elements of pollution in road dust. This study evaluated the use of road dust color as a proxy methodology to identify samples contaminated with presumably contaminating elements. FRX determined the concentrations of Fe, Ti, Rb, Sr, Y, Cu, Zn, and Pb in eighty-five road dust samples. The appliance of the RGB system and the Munsell color cards identified five color groups of road dust samples. The discriminant analysis validated these groups by colorimetric indices and presumably contaminating elements. The “very dark gray” color of road dust contains the highest concentrations of Pb, Cu, Zn, and Y. The redness and saturation rates showed high correlations with presumably contaminating elements in “dark gray” and “very dark gray” color samples. The color of road dust, as a proxy technique, allows identifying samples contaminated with presumably contaminating elements.

Abstract Foam-mat drying has been recently welcomed as an effective technique since it lacks deficiencies that plague conventional methods including poor water absorption and long process time. In this study, effect of hot air temperature (50, 60 and 70 °C) on quality properties of lime juice was investigated during foam-mat drying. The process was also simulated to determine the effect of temperature on the uniformity of moisture and temperature distributions during drying. Increase in temperature improved powder flowability. With a decrease in temperature, water absorption index increased by 17.74%. Moreover, effective moisture diffusion coefficient increased as a result of a rise in temperature, which, in turn, increased the drying rate by 20.23%. Increase in temperature also resulted in a 1.80% increase in the total color change and 6.60%, 1.18% and 1.35% decrease in chroma index, hue angle and browning index, respectively. A correlation coefficient of 0.903 was achieved between experimental and simulated data. Uniform distributions of temperature and moisture during drying resulted in an improvement in the qualitative properties of the final product regarding the uniformity of the powder's moisture content, improvement of water absorption properties and, finally, increase in the product's quality. Graphical abstract 3D moisture content profile at the end of drying process at air temperature 70 °C. Image 1 Highlights • Experimental and numerical foam-mat drying of lime juice was examined. • Increase in temperature from 50 to 70 °C resulted in a 20.23% rise in drying rate. • Uniform temperature and moisture distributions were obtained during the process. • 17.74% improvement in the powder's water absorption index was noticed at 50 °C. • The methodology may be a desirable counterpart for industrial applications. [ABSTRACT FROM AUTHOR]

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Canopy color and structure can strongly reflect plant functions. Color characteristics and plant height as well as canopy breadth are important aspects of the canopy phenotype of soybean plants. High-throughput phenotyping systems with imaging capabilities providing color and depth information can rapidly acquire data of soybean plants, making it possible to quantify and monitor soybean canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze soybean canopy development under natural light conditions. Thus, a Kinect sensor-based high-throughput phenotyping (HTP) platform was developed for soybean plant phenotyping. To calculate color traits accurately, the distortion phenomenon of color images was first registered in accordance with the principle of three primary colors and color constancy. Then, the registered color images were applied to depth images for the reconstruction of the colorized three-dimensional canopy structure. Furthermore, the 3D point cloud of soybean canopies was extracted from the background according to adjusted threshold, and each area of individual potted soybean plants in the depth images was segmented for the calculation of phenotypic traits. Finally, color indices, plant height and canopy breadth were assessed based on 3D point cloud of soybean canopies. The results showed that the maximum error of registration for the R, G, and B bands in the dataset was 1.26%, 1.09%, and 0.75%, respectively. Correlation analysis between the sensors and manual measurements yielded R2 values of 0.99, 0.89, and 0.89 for plant height, canopy breadth in the west-east (W−E) direction, and canopy breadth in the north-south (N−S) direction, and R2 values of 0.82, 0.79, and 0.80 for color indices h, s, and i, respectively. Given these results, the proposed approaches provide new opportunities for the identification of the quantitative traits that control canopy structure in genetic/genomic studies or for soybean yield prediction in breeding programs.

The conditions for obtaining and the rules for changing the color indices of ceramic pigments in the system CaO-MgO-AlO-SiO-CrO are established. The main phase of such pigments is chromium garnet, formed with the direct participation of blast-furnace slag minerals. It is shown that as the acidity of pigment batches is increased by equimolecular substitution of free silicon dioxide in their composition based on phosphorus oxide (V) the purity and intensity of the green color of uvarovite pigments intensifies and the firing temperature of the pigments increases to 1100 - 1150°C. [ABSTRACT FROM AUTHOR]

In this study, hazelnut kernels were dried in an infrared dryer with microwave pretreatment. To determine the optimization conditions of the hazelnut samples, response surface methodology and central composite design were used to investigate the effect of temperature levels (45, 65 and 85 ° C), infrared power levels (500, 1000 and 1500 W) and microwave power levels (270, 450 and 630 W) on drying of hazelnut kernels. The adequacy and accuracy of the fitted models (Lack of Fit and R2 ) were checked with analysis of variance (ANOVA). Response surfaces and contour plots were created to show the interaction between the independent and the response variables. Based on the experiments, the optimum conditions for the highest values of effective moisture diffusivity and L* (color index) and the lowest values of consumption energy, shrinkage and a* , b* (color indices) were determined at air temperature of 45°C, microwave power of 470.86 W and infrared power of 1316.72 W. The optimum values of the response variables affecting the effective moisture diffusivity, consumption energy, shrinkage, L*, a* and b* were obtained 2.35×10-9 m 2 s-1, 2.65 kWh, 12.94%, 54.10, 14.97 and 16.70, respectively. [ABSTRACT FROM AUTHOR]

Space Technology provides a resourceful-cost effective means to assess soil nutrients essential for soil management plan. Soil organic matter (SOM) is one of valuable controlling productivity of crops by providing nutrient in farming systems. Geospatial modeling of soil organic matter is essential if there is unavailability of soil test laboratories and its strong spatial correlation. In the present analysis, soil organic matter is modeled from satellite image derived spectral color indices. Brightness Index (BI), Coloration Index (CI), Hue Index (HI), Redness Index (RI) and Saturation Index (SI) were calculated by converting DN value to radiance and radiance to reflectance from Thematic Mapper image. Geospatial model was developed by regressing SOM with color indices and producing multiple regression model using stepwise regression technique. The multiple regression equation between SOM and spectral indices was significant with R = 0. 56 at 95% confidence level. The resulting MLR equation was then used for the spatial prediction for the entire study area. Redness Index was found higher significance in estimating the SOM. It was used to predict SOM as auxiliary variables using cokringing spatial interpolation technique. It was tested in seven VDCs of Chitwan district of Nepal using Thematic Mapper remotely sensed data. SOM was found to be measured ranging from 0.15% to 4.75 %, with a mean of 2.24 %. Remotely sensed data derived spectral color indices have the potential as useful auxiliary variables for estimating SOM content to generate soil fertility management plans. [ABSTRACT FROM AUTHOR]

Abstract: Fall biological processes are driven by a combination of environmental factors, with cumulative effects over the length of the growing season, which are currently difficult to model. This study evaluated if leaf color change in fall (i.e., leaf yellowing) and brook trout spawning could be two biological processes that are synchronized at a regional scale and if leaf yellowing could be used to determine the peak of spawning activity of this species. To this end, we surveyed 551 brook trout redds and examined 193 digital images of forest trees in the Laurentides Wildlife Reserve (Québec, Canada) over the fall season. Results showed that leaf yellowing and brook trout spawning were synchronized, providing one of the first examples of temporal matching between freshwater and terrestrial biological processes at the regional scale. Considering the difficulty of monitoring the phenology of freshwater fish spawning at high spatial and temporal resolution, terrestrial integrators of environmental conditions, such as leaf color change, may prove to be promising predictors of spawning activity in the management of fish populations. [Copyright &y& Elsevier]

We have examined the relationship between the morphological types and the u-r, g-i, and r-z color indices of 365 pairs of galaxies with radial velocities 3000

The design of proxy techniques is an innovative tool to monitor the potentially toxic elements of pollution in road dust. This study evaluated the use of road dust color as a proxy methodology to identify samples contaminated with presumably contaminating elements. FRX determined the concentrations of Fe, Ti, Rb, Sr, Y, Cu, Zn, and Pb in eighty-five road dust samples. The appliance of the RGB system and the Munsell color cards identified five color groups of road dust samples. The discriminant analysis validated these groups by colorimetric indices and presumably contaminating elements. The "very dark gray" color of road dust contains the highest concentrations of Pb, Cu, Zn, and Y. The redness and saturation rates showed high correlations with presumably contaminating elements in "dark gray" and "very dark gray" color samples. The color of road dust, as a proxy technique, allows identifying samples contaminated with presumably contaminating elements. [ABSTRACT FROM AUTHOR]

Canopy color and structure can strongly reflect plant functions. Color characteristics and plant height as well as canopy breadth are important aspects of the canopy phenotype of soybean plants. High-throughput phenotyping systems with imaging capabilities providing color and depth information can rapidly acquire data of soybean plants, making it possible to quantify and monitor soybean canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze soybean canopy development under natural light conditions. Thus, a Kinect sensor-based high-throughput phenotyping (HTP) platform was developed for soybean plant phenotyping. To calculate color traits accurately, the distortion phenomenon of color images was first registered in accordance with the principle of three primary colors and color constancy. Then, the registered color images were applied to depth images for the reconstruction of the colorized three-dimensional canopy structure. Furthermore, the 3D point cloud of soybean canopies was extracted from the background according to adjusted threshold, and each area of individual potted soybean plants in the depth images was segmented for the calculation of phenotypic traits. Finally, color indices, plant height and canopy breadth were assessed based on 3D point cloud of soybean canopies. The results showed that the maximum error of registration for the R, G, and B bands in the dataset was 1.26%, 1.09%, and 0.75%, respectively. Correlation analysis between the sensors and manual measurements yielded R2 values of 0.99, 0.89, and 0.89 for plant height, canopy breadth in the west-east (W–E) direction, and canopy breadth in the north-south (N–S) direction, and R2 values of 0.82, 0.79, and 0.80 for color indices h, s, and i, respectively. Given these results, the proposed approaches provide new opportunities for the identification of the quantitative traits that control canopy structure in genetic/genomic studies or for soybean yield prediction in breeding programs. [ABSTRACT FROM AUTHOR]