The extraction of device-independent color values from affordable and accessible digital images based on a standard color space system is crucially necessary for agricultural applications, where color information for plant monitoring or diagnostics is required. This study aimed to develop a transformation matrix for obtaining XYZ color coordinates from the RGB values of digital images for agricultural applications. The calibration procedure was based on Munsell and Macbeth color charts. The color coordinates of eight color charts were measured, and the transformation matrices were built. Leaf samples of six different plants were used and compared based on the proposed transformation technique. The actual XYZ values of plant leaves were measured, and the RGB values were derived from the digital images. The results indicate that the Macbeth color chart with 24 colors had the best performance, with an average ∆ELAB and CIEDE2000 of less than 1.77 and 1.97, respectively. The findings demonstrate that the proposed transformation matrix was successful in converting RGB values to XYZ values and can be employed as a quick, easy, and inexpensive technique for obtaining standard color information.

A key factor in the appearance of metal flake-pigmented coatings (MF-PCs) is the orientation of the metal flakes, which affects the coating's luster (at large distances) and visual texture (up close). This study introduces a dome-based reflectance transformation imaging (RTI) system to estimate metal flakes’ orientation using intensity multi-light image collections (MLICs) and an algebraic reflectance map inversion algorithm. The flake's angles and 2D/3D probability density functions (PDFs) were derived from this, and a new orientation index (OI) for MF-PCs. The impact of metal flakes’ orientation on appearance was assessed using the lightness flop index (LFI), sparkle (SG), and graininess grades (GG). Results show that the LFI and GG increase as the OI decreases, while SG at 15° rises with higher orientation but decreases at 45° and 75°. Additionally, a non-linear negative exponential relationship between the OI and LFI was observed, highlighting orientation as a significant dimension in predicting MF-PC’s appearance.

Accurate color measurement is essential for quality control in white powdered foods such as sugar. While spectrophotometers are the reference instruments, their cost limits routine use in resource limited settings. Flatbed scanners provide an affordable alternative, but their accuracy depends strongly on technical settings and sample presentation. This study systematically optimized a commercial flatbed scanner parameters (power supply, gamma correction, internal color management, image format, bit depth, and resolution) and physical variables (container dimensions, cover type, and substrate) for a sugar sample color measurement. Spectrophotometric measurements (D65 illuminant, 2° observer) served as the reference, and color differences were quantified using ΔE₀₀. Images captured via an Epson flatbed scanner were processed to derive mean RGB values. Results show that operating the scanner under mains power, using manual gamma correction (γ = 2.2), 24-bit TIFF format, and 300 dpi resolution, minimized the color difference to ΔE₀₀ ≈ 4.07, significantly improving accuracy compared to internal color management profiles (ΔE₀₀ ≈ 6.9 – 7.2). Increasing bit depth to 48-bit or resolution beyond 300 dpi yielded negligible gains. Physically, a non-fluorescent white cover, quartz substrate, and sample container diameter ≥ 4 cm further improved repeatability. Three way ANOVA confirmed significant interactions (p<0.001) between cover type and diameter. The study concludes that while the scanner does not yet meet strict industrial tolerances (ΔE₀₀ <2), it provides a repeatable and cost-effective alternative for relative color measurement quality screening of white powdered foods when proper optimization protocols are followed in resource-limited settings.

Metal flake-pigmented coatings are widely appreciated for their eye-catching appearance where understanding their physical and optical properties is crucial for the automotive coatings industry. The metal flakes loaded in these coatings are optically anisotropic, with their sparkle change based on illumination and viewing angles. These tiny micro-mirrors exhibit various local angular reflections (LARni), and it is interesting to characterize their type-dependent optical behavior. In this study, we employed a reflectance transformation imaging (RTI) system to quantify the mean local angular reflectance (M-LARe) index of metal flakes in automotive metallic coatings. We first captured multi-light image collections (MLICs) from seven automotive metal flake-pigmented coatings, followed by a series of processing steps to extract the flakes' LARni. The extracted LARni were then normalized, both radiometrically and geometrically, and used to compute the proposed type-dependent index, which is independent of device as well as flake size and orientation. Finally, the M-LARe index histograms of the coatings were derived, and the seven samples were ranked according to their reflective capacity. We also observed a meaningful dependency between the mean value of the proposed M-LARe indices for each sample and its sparkle grade at 15°. Potential uncertainties, such as resin type, coating thickness, and metal flake count, were also acknowledged.

The visual whiteness of sugar is a key factor in determining its quality and purity. Consequently, the development of rapid and cost-effective methods for the color evaluation of sugar samples is of critical importance to the sugar industry. This study aims to introduce a method for the measurement of whiteness index (WI) and yellowness index (YI) of sugar based on a commercial digital scanner. In this regard, sugar samples were collected across three different cultivation seasons. The color images of sugar samples were obtained using a digital scanner, and the color indices were extracted based on four WI and two YI formulas. The results were then compared with the standard color measurement technique based on spectrophotometry. It was observed that the proposed technique led to a correlation coefficient of 0.947 for the estimation of WI. For both YI formulas, the correlation of 0.944 was obtained. The results indicate that the proposed technique has a remarkable performance in the estimation of sugar color indices and offers a simple, easy-to-implement and fast technique for industry.

This paper proposes a novel approach for optimizing the coverage and deployment of Wireless Sensor Networks (WSNs) using image processing techniques. The proposed method involves the placement of an initial grid network on the map image, followed by the gradual decrease or increase of the radius of the initial operation and the adjustment of the placement of the nodes while also maintaining full connectivity communication between the sensors deployed. Our experiments demonstrate that this approach is effective in achieving the desired coverage and deployment of the wireless sensor network. It ensures efficient coverage and fully connectivity and can be applied to various application scenarios.