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Background: Leaf area index (LAI) is an important indicator for assessing plant growth and development, and is also closely related to photosynthesis in plants. The realization of rapid accurate estimation of crop LAI plays an important role in guiding farmland production. In study, the UAV‐RGB technology was used to estimate LAI based on 65 winter wheat varieties at different fertility periods, the wheat varieties including farm varieties, main cultivars, new lines, core germplasm and foreign varieties. Color indices (CIs) and texture features were extracted from RGB images to determine their quantitative link to LAI. Results: The results revealed that among the extracted image features, LAI exhibited a significant positive correlation with CIs (r = 0.801), whereas there was a significant negative correlation with texture features (r = −0.783). Furthermore, the visible atmospheric resistance index, the green–red vegetation index, the modified green–red vegetation index in the CIs, and the mean in the texture features demonstrated a strong correlation with the LAI with r > 0.8. With reference to the model input variables, the backpropagation neural network (BPNN) model of LAI based on the CIs and texture features (R2 = 0.730, RMSE = 0.691, RPD = 1.927) outperformed other models constructed by individual variables. Conclusion: This study offers a theoretical basis and technical reference for precise monitor on winter wheat LAI based on consumer‐level UAVs. The BPNN model, incorporating CIs and texture features, proved to be superior in estimating LAI, and offered a reliable method for monitoring the growth of winter wheat. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Positional and multicolor photometric observations of near-Earth asteroids were carried out using the SBG telescope of the Kourovka Astronomical Observatory of Ural Federal University and the Zeiss-1000 telescope of the Simeiz Observatory of INASAN in 2022–2023. Based on the results of positional observations with the SBG telescope, improved orbital elements were obtained for seven asteroids. The periods of axial rotation of seven asteroids were estimated from photometric observations. Based on the results of photometric observations in the B, V, R, I filters, color indices were obtained for six asteroids. [ABSTRACT FROM AUTHOR]

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]

The results of photometric observations of two bright blazars of the northern hemisphere, namely, BL Lacertae and 1ES 1426+428, during 2018–2020 through BVRI filters of the Johnson/Bessel system are given. The observations were performed with the two telescopes: AZT-8 of observation station Lisnyky of the Astronomical Observatory of the Taras Shevchenko National University of Kyiv (Kyiv oblast, Ukraine) and Zeiss-600 of high-altitude observatory Peak Terskol of the International Center for Astronomical, Medical, and Ecological Research (IC AMER) of the National Academy of Sciences of Ukraine. In total, more than sixty nights of observations were recorded and processed. The main goals have been in performing a cross-matching analysis of blazar light curves in BVRI bands to detect the short-term variability (STV) and long-term variability (LTV) and to investigate the chromaticity of color parameters. For both objects, fluctuations of brightness up to 1m in 2018–2020 were recorded in the BVRI bands of the Johnson/Bessel system with a total error of 0.03m–0.1m. The intraday variability (IDV) was revealed for BL Lacertae November 17/November 18, 2018. During the calculations of the color indices, the trend of bluish color with an increase in the brightness (bluer-when-brighter, BWB) was found. For BL Lacertae, the LTV was reliably detected by using different pairs of filters. Also, the BWB trend with an average correlation (over 0.5) was recorded for 1ES 1426+428. The presence of such fluctuations in the color of blazars was due to the synchrotron radiation of the jet. The revealed partial correlation of variations in brightness with low time resolution (more than a week) between the photometric optical observations and the data of the Fermi gamma-ray telescope in 2018–2020 require additional research. [ABSTRACT FROM AUTHOR]

A series of ceramics in the system CaO-Cr2O3-MgO-SiO2 was synthesized via solid-state high temperature sintering at 1000, 1100 and 1200 °C. The resulting ceramics were examined by powder X-ray diffraction, scanning electron microscopy, infrared and electron paramagnetic resonance spectroscopy. The color characteristics were measured spectrophotometrically. It was found that under the synthesis conditions multiphase ceramics were obtained which contain diopside, wollastonite, magnesiochromite, cristobalite, tridymite and periclase in various proportions. The synthesized ceramics are gray, gray-green and green in color, depending on the content of Cr additive, temperature of furnace and phase composition. [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.

We present photometric observations of 25 small asteroids from different families (Eos, Erigone, Flora, Maria, Massalia, Phocaea and Zita) and dynamical groups. We derive their synodic periods and lightcurve amplitudes, as well as V–R color indices, and absolute magnitudes. Furthermore, we revise their albedos using the determined absolute magnitudes. Three of the objects are studied for the first time. We also make estimates of the diameters of four asteroids. Many of the observed asteroids have short rotation periods and small amplitudes of the lightcurves, which may be indicative of their potential binary nature. Our photometry in combination with future data can be used in further studies. Analysis of the parameters obtained for the observed asteroids, such as absolute magnitudes, revised albedos, as well as color indices obtained from the SDSS and Sky-Mapper surveys, makes it possible to effectively classify them taxonomically. • Photometry of asteroids. • The families of asteroids. • Lightcurves and rotational period, of small asteroids. • Color indices and absolute brightness of small asteroids. • Albedo of asteroids. [ABSTRACT FROM AUTHOR]