Search Results (153)

We developed an optoelectronic sensor capable of quantifying in-plane rotational and linear displacements between two parallel surfaces. The sensor utilizes a photo detector to capture the intensity of red (R), green (G), blue (B), and clear (C, broad visible spectrum) light reflected from a color gradient wheel on the opposing surface. Variations in reflected R, G, B and C light intensities, caused by displacements, were used to predict linear and rotational motion via a polynomial regression algorithm. To train and validate this model, we employed a custom-built positioning stage that produced controlled displacement and rotation while recording corresponding changes in light intensity. The reliability of the predicted linear and rotational displacement results was evaluated using two different color gradient wheels: a wheel with changing color hue, and another wheel with changing color hue and saturation. Benchtop experiments demonstrated high predictive accuracy, with coefficients of determination (R²) exceeding 0.94 for the hue-only wheel and 0.92 for the hue-and-saturation wheel. These results highlight the sensor’s potential for detecting shear displacement and rotation in footwear and wearable medical devices, such as orthotics and prostheses, enabling the detection of slippage, overfitting, or underfitting. This capability is particularly relevant to clinical conditions, including diabetic neuropathy, flat feet, and limb amputations. Full article

The chlorophyll content is an important index reflecting the growth status and nutritional level of plants. The rapid, accurate and nondestructive monitoring of the SPAD content of apple trees can provide a basis for large-scale monitoring and scientific management of the growth status of apple trees. In this study, the canopy leaves of apple trees at different growth stages in the same year were taken as the research object, and remote sensing images of fruit trees in different growth stages (flower-falling stage, fruit-setting stage, fruit expansion stage, fruit-coloring stage and fruit-maturing stage) were acquired via a DJI MAVIC 3 multispectral unmanned aerial vehicle (UAV). Then, the spectral reflectance was extracted to calculate 15 common vegetation indexes as eigenvalues, the 5 vegetation indexes with the highest correlation were screened out through Pearson correlation analysis as the feature combination, and the measured SPAD values in the leaves of the fruit trees were gained using a handheld chlorophyll meter in the same stages. The estimation models for the SPAD values in different growth stages were, respectively, established through five machine learning algorithms: multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost). Additionally, the model performance was assessed by selecting the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE). The results show that the SPAD estimation results vary from stage to stage, where the best estimation model for the flower-falling stage, fruit-setting stage and fruit-maturing stage is RF and those for the fruit expansion stage and fruit-coloring stage are PLSR and MLR, respectively. Among the estimation models in the different growth stages, the model accuracy for the fruit expansion stage is the highest, with R² = 0.787, RMSE = 0.87 and MAE = 0.644. The RF model, which outperforms the other models in terms of the prediction effect in multiple growth stages, can effectively predict the SPAD value in the leaves of apple trees and provide a reference for the growth status monitoring and precise management of orchards. Full article

A rapid, traceable, and highly sensitive method was developed for the simultaneous separation and quantification of 24 water-soluble synthetic colorants in premade cocktails, utilizing ultra-performance liquid chromatography coupled with diode array detection (UPLC-DAD). The purity of each colorant was individually confirmed through multi-wavelength analysis. Chromatographic conditions, including mobile phase composition and gradient elution, were meticulously optimized, achieving the separation of the 24 colorants on a BEH C18 column using a linear gradient elution within 16 min. The mobile phase consisted of an ammonium acetate solution (100 mmol/L, pH 6.25) and a mixed organic solvent of methanol and acetonitrile (2:8, v/v). The method exhibited excellent linearity across the concentration range of 0.005–10 μg/mL, with limits of detection (LODs) ranging from 0.66 to 27.78 μg/L for all 24 colorants. The method also demonstrated good precision (0.1–4.9%) at various concentration levels and recoveries ranging from 87.8% to 104.5% at spiked concentrations of 0.1, 0.5, and 1.0 μg/mL. A comparison with other published methods for colorant determination in food samples using HPLC-DAD and LC-MS (2014–2024) revealed that the proposed method offers superior performance in terms of the number of analytes detected, lower limits of detection, and reduced analytical time. Finally, the method was successfully applied to the analysis of colorants in premade cocktails from different sources. Full article

This study evaluates the optical and thermal performance of near-infrared (NIR)-blocking spectacle lenses at luminous transmittance grades of 0, 2, and 3. Ten lens types were tested, including clear, tinted, and NIR-blocking spectacle lenses (NIBSL). The NIR blocking rate was measured across 780–1100 nm and 1100–1400 nm wavelength bands. Color reproduction was assessed using sharpness (MTF 50), point spread function (PSF), and color accuracy (ΔE₀₀) under 1000 lux outdoor illumination. Thermal insulation was analyzed by monitoring porcine skin temperature at 36 °C and 60 °C under each lens type. As a result, the NIBSL showed better near-infrared blocking performance than other types of lenses in both wavelength ranges, and the coated NIBSL blocked near-infrared more effectively than the polymerized lenses. Compared with other types of lenses, NIBSL showed no difference in object identification, color recognition, and reproducibility, so there is no problem in using them together. Strong correlations were observed between lens surface temperature and underlying pig skin temperature, and inverse correlations between NIR blocking rate and pig skin temperature gradient. These findings confirm that NIBSL offer enhanced protection against NIR-induced thermal effects without compromising optical performance, supporting their use in daily environments for ocular and skin safety. Full article

Cryptochrome is an important class of blue-light receptors involved in various physiological activities such as photomorphogenesis and abiotic stress regulation in plants. In order to investigate the molecular mechanism of blue-light-induced color change in Chimonobambusa sichuanensis, we screened and cloned the gene encoding the blue-light receptor Cryptochrome. In order to investigate the molecular mechanism of blue-light-induced color change in Chimonobambusa sichuanensis, we screened and cloned the gene encoding the blue-light receptor Cryptochrome in Ch.sichuanensis, and analyzed the expression characteristics of the Cryptochrome gene in Ch.sichuanensis under different light intensities, light quality, and temperatures by qRT-PCR. Through homologous cloning, a total of four CsCRY genes were obtained in the Ch.sichuanensis genome, namely, CsCRY1a, CsCRY1b, CsCRY2, and CsCRY3. Structural domain analyses of the encoded proteins of the four genes revealed that all CsCRYs proteins had the typical photoreceptor structural domain, PRK (protein kinase C-related kinase). Phylogenetic tree analyses revealed that the four genes CsCRY1a, CsCRY1b, CsCRY2, and CsCRY3 could be categorized into three subfamilies, with CsCRY1a and CsCRY1b clustered in subfamily I, CsCRY2 classified in subfamily II, and CsCRY3 belonging to subfamily III. All CsCRYs proteins lacked signal peptides and the instability index was higher than 40, among which the isoelectric points of CsCRY1a, CsCRY1b, and CsCRY2 were around five. qRT-PCR analysis revealed that the expression of all four CsCRYs genes was up-regulated at 75 µmol·m⁻²·s⁻¹ blue-light illumination for 4 h. In addition, under treatments of different light quality, the expression of CsCRY2 genes was significantly higher under blue light than under red light and a mixture of red light and blue light with a light intensity of 1:1; the expression of CsCRY1a and CsCSY1b was significantly higher in the mixed light of red and blue light than in the single light treatment, while under different temperature gradients, CsCRYs genes were highly expressed under low-temperature stress at −5 °C and 0 °C. This study provides a basis for further research on blue-light-induced color change in Ch.sichuanensis and expands the scope of Cryptochrome gene research. Full article

Accurate crop yield prediction is essential for sugarcane growers, as it enables them to predict harvested biomass, guiding critical decisions regarding acquiring agricultural inputs such as fertilizers and pesticides, the timing and execution of harvest operations, and cane field renewal strategies. This study is based on an experiment conducted by researchers from the Commonwealth Scientific and Industrial Research Organisation (CSIRO), who employed a UAV-mounted LiDAR and multispectral imaging sensors to monitor two sugarcane field trials subjected to varying nitrogen (N) fertilization regimes in the Wet Tropics region of Australia. The predictive performance of models utilizing multispectral features, LiDAR-derived features, and a fusion of both modalities was evaluated against a benchmark model based on the Normalized Difference Vegetation Index (NDVI). This work utilizes the dataset produced by this experiment, incorporating other regressors and features derived from those collected in the field. Typically, crop yield prediction relies on features derived from direct field observations, either gathered through sensor measurements or manual data collection. However, enhancing prediction models by incorporating new features extracted through regressions executed on the original dataset features can potentially improve predictive outcomes. These extracted features, nominated in this work as meta-features (MFs), extracted through regressions with different regressors on original features, and incorporated into the dataset as new feature predictors, can be utilized in further regression analyses to optimize crop yield prediction. This study investigates the potential of generating MFs as an innovation to enhance sugarcane crop yield predictions. MFs were generated based on the values obtained by different regressors applied to the features collected in the field, allowing for evaluating which approaches offered superior predictive performance within the dataset. The kNN meta-regressor outperforms other regressors because it takes advantage of the proximity of MFs, which was checked through a projection where the dispersion of points can be measured. A comparative analysis is presented with a projection based on the Uniform Manifold Approximation and Projection (UMAP) algorithm, showing that MFs had more proximity than the original features when projected, which demonstrates that MFs revealed a clear formation of well-defined clusters, with most points within each group sharing the same color, suggesting greater uniformity in the predicted values. Incorporating these MFs into subsequent regression models demonstrated improved performance, with ${\overline{R}}^2$ values higher than 0.9 for MF Grad Boost M3, MF GradientBoost M5, and all kNN MFs and reduced error margins compared to field-measured yield values. The ${\overline{R}}^2$ values obtained in this work ranged above 0.98 for the AdaBoost meta-regressor applied to MFs, which were obtained from kNN regression on five models created by the researchers of CSIRO, and around 0.99 for the kNN meta-regressor applied to MFs obtained from kNN regression on these five models. Full article

The lattice Boltzmann method (LBM) is widely applied to model the pore-scale two-phase flow of immiscible fluids through porous media, and one common variant of the LBM is the color gradient method (CGM). However, in the literature, many competing algorithms have been proposed for accomplishing different steps in the CGM. Therefore, this paper is the first in a series that aims to critically review and evaluate different algorithms and methodologies that have been proposed for use in the CGM. Specifically, in this paper, we (1) provide a brief introduction to the LBM and CGM that enables and facilitates consideration of more sophisticated topics subsequently; (2) compare three methods for modeling the behavior of fluids of moderately different viscosities; (3) compare two methods for calculating the color gradient; and (4) compare two methods for modeling external forces or accelerations acting upon the fluids of interest. These topics are selected for the first paper in the series because proper selection of these algorithms is necessary and sufficient to perform two common “benchmark” simulations, namely bubble tests and layered Poiseuille flow. Future papers in the series will build upon these topics, considering more challenging conditions or phenomena. By systematically reviewing key aspects, features, capabilities, and limitations of the CGM, this series of papers will extend our collective ability to apply the method to a variety of important fluid flow problems in geosciences and engineering. Full article

Most dyeing occurs when a fabric is in a wet state, while color matching is performed when the fabric is in a dry state. As water is a colorless liquid, it has been difficult to analytically map these two states using existing color theories. Machine learning models provide a heuristic approach to this class of problems. Linear regression, random forest, eXtreme Gradient Boosting (XGBoost), and multiple neural network models were constructed and compared to predict the color of dry cotton fabric from its wet state. Different models were developed based on squeeze pressure (water pickup), with inputs to the models consisting of the L*a*b* (L*: lightness; a*: red–green axis; b*: blue–yellow axis) coordinates in the wet state and the outputs of the models consisting of the predicted L*a*b* coordinates in the dry state. The neural network model performed the best by correctly predicting the final shade to under a 1.0 color difference unit using the International Commission on Illumination (CIE) 2000 color difference formula (CIEDE2000) color difference equation about 63.9% of the time. While slightly less accurate, XGBoost and other tree-based models could be trained in a fraction of the time. Full article

The current image registration models have problems such as low feature point matching accuracy, high memory consumption, and significant computational complexity in heterogeneous image registration, especially in complex environments. In this context, significant differences in lighting and leaf occlusion in orchards can result in inaccurate feature extraction during heterogeneous image registration. To address these issues, this study proposes an AD-ResSug model for heterogeneous image registration. First, a VGG16 network was included as the encoder in the feature point encoder system, and the positional encoding was embedded into the network. This enabled us to better understand the spatial relationships between feature points. The addition of residual structures to the feature point encoder aimed to solve the gradient diffusion problem and enhance the flexibility and scalability of the architecture. Then, we used the Sinkhorn AutoDiff algorithm to iteratively optimize and solve the optimal transmission problem, achieving optimal matching between feature points. Finally, we carried out network pruning and compression operations to minimize parameters and computation cost while maintaining the model’s performance. This new AD-ResSug model uses evaluation indicators such as peak signal-to-noise ratio and root mean square error as well as registration efficiency. The proposed method achieved robust and efficient registration performance, verified through experimental results and quantitative comparisons of processing color with ToF images captured using heterogeneous cameras in natural apple orchards. Full article

This study assesses the accuracy of Local Climate Zone (LCZ) classification and its impact on land surface temperature (LST) analysis in Mediterranean cities using high-resolution ECOSTRESS data. Two classification methods were compared: a Geographic Information System (GIS)-based approach integrating high-resolution geospatial data and an LCZ map derived from WUDAPT. Discrepancies in LCZ classification influenced the spatial distribution of urban forms, with WUDAPT overestimating LCZ 6 (open low-rise) and LCZ 8 (large low-rise) while underrepresenting more compact urban types. LST analysis revealed distinct thermal responses between Milan and Lecce, underscoring the influence of urban morphology and local climate. Densely built zones (LCZ 2, LCZ 5) exhibited the highest temperatures, especially at night, while LCZ 8 also retained significant heat. Milan’s dense urban areas experienced pronounced nighttime overheating, whereas Lecce showed a clear daytime temperature gradient, with historic districts (LCZ 2) maintaining lower LST the light-colored and high thermal capacity of building materials. A Kruskal–Wallis test confirmed significant differences between the GIS-based and WUDAPT-derived LCZ maps, highlighting the impact of classification methodology and spatial resolution on LST analysis. These findings emphasize the need for multi-scale approaches to urban climate adaptation and mitigation, providing valuable advice for urban planners and policymakers in development of sustainable and climate-resilient cities. This research is also among the first to integrate ECOSTRESS data with LCZ maps to examine LST variations across spatial and temporal scales. Full article

Infrared imaging is based on thermal radiation and does not rely on visible light, allowing for it to operate normally at night and in low-light conditions. This characteristic is beneficial for regulatory authorities to monitor ships. Existing infrared image segmentation methods face challenges such as the absence of color information, blurred edges, weak high-frequency details, and low contrast due to the imaging principles. Consequently, the segmentation accuracy for small-sized ship targets and edges is low, influenced by the indistinct features of infrared images and the weak difference between the background and targets. To address these issues, this paper proposes an infrared image ship segmentation algorithm called the Infrared Image Edge-Enhanced Segmentation Network (IERNet) to extract ship temperature information. By using pseudo-color infrared images, the sensitivity to edges is enhanced, improving the edge features of ships in infrared images. The Sobel operator is used to obtain edge feature maps, and the Convolutional Block Attention Module (CBAM) extracts key feature information. In the Fusion Unit, edge features guide the extraction of infrared ship features in the backbone network, resulting in feature maps rich in edge information. Finally, a specialized loss function with edge weights supervises the fusion features. An eXtreme Gradient Boosting (XGBoost) machine learning model is then established to predict the ship image brightness temperature threshold, using engine brightness threshold, water area brightness threshold, boundary brightness threshold, and temperature gradient as predictive elements. In terms of image segmentation, our algorithm achieves a segmentation performance of 89.17% mIoU. Regarding the XGBoost model’s performance, it achieves high goodness of fit and small error values on both the training and testing sets, demonstrating its good performance in predicting ship temperature. The model achieves over 70% goodness of fit, and the RMSE values for both models are 3.472, indicating minimal errors. Statistical analysis reveals that the proportion of ship temperature differences predicted by the XGBoost model exceeding 2 is less than 0.020%. The proposed temperature detection method offers higher accuracy and versatility, contributing to more efficient detection of abnormal ship temperatures at night. Full article

Tea (Camellia sinensis L.) holds agricultural economic value and forestry carbon sequestration potential, with Taiwan’s annual tea production exceeding TWD 7 billion. However, climate change-induced stressors threaten tea plant growth, photosynthesis, yield, and quality, necessitating an accurate real-time monitoring system to enhance plantation management and production stability. This study surveys tea plantations at low, mid-, and high elevations in Nantou County, central Taiwan, collecting data from 21 fields using conventional farming methods (CFMs), which emphasize intensive management, and agroecological farming methods (AFMs), which prioritize environmental sustainability. This study integrates leaf area index (LAI), photochemical reflectance index (PRI), and quantum yield of photosystem II (ΦPSII) data with unmanned aerial vehicles (UAV)-derived visible-light and multispectral imagery to compute color indices (CIs) and multispectral indices (MIs). Using feature ranking methods, an optimized dataset was developed, and the predictive performance of eight regression algorithms was assessed for estimating tea plant physiological parameters. The results indicate that LAI was generally lower in AFMs, suggesting reduced leaf growth density and potential yield differences. However, PRI and ΦPSII values revealed greater environmental adaptability and potential long-term ecological benefits in AFMs compared to CFMs. Among regression models, MIs provided greater stability for tea plant physiological parameters, whereas feature ranking methods had minimal impact on accuracy. XGBoost outperformed all models in predicting parameters, achieving optimal results for (1) LAI: R² = 0.716, RMSE = 1.01, MAE = 0.683, (2) PRI: R² = 0.643, RMSE = 0.013, MAE = 0.009, and (3) ΦPSII: R² = 0.920, RMSE = 0.048, MAE = 0.013. Overall, we highlight the effectiveness of integrating gradient boosting models with multispectral data to capture tea plant physiological characteristics. This study develops generalizable predictive models for tea plant physiological parameter estimation and advances non-contact crop physiological monitoring for tea plantation management, providing a scientific foundation for precision agriculture applications. Full article

Underwater images frequently experience degradation, including color shifts, blurred details, and reduced contrast, primarily caused by light scattering and the challenging underwater conditions. The conventional methods based on physical models have proven insufficient for effectively addressing diverse underwater conditions, while deep learning approaches are limited by the quantity and diversity of data, making it challenging to perform well in unknown environments. Furthermore, these methods typically fail to fully exploit the spectral differences between clear and degraded images and do not capture critical information in the frequency domain, limiting further improvements in enhancement performance. In order to tackle these challenges, we introduce PCAFA-Net, a physically guided network designed for enhancing underwater images through adaptive adjustment in multiple color spaces and the use of frequency–spatial attention. Our proposed model is made up of three essential modules: the Adaptive Gradient Simulation Module (AGSM), which models the degradation mechanism of underwater images; the Adaptive Color Range Adjustment Module (ACRAM), which adaptively modifies the histogram distributions across RGB, Lab, and HIS color spaces; and the Frequency–Spatial Strip Attention Module (FSSAM), which fully utilizes both frequency and spatial domain information. Extensive experiments were conducted on three datasets, demonstrating that our proposed method outperforms others in both subjective and objective evaluations. Full article

Green, newly felled bamboo stems are prone to cracking during the drying process due to the growth stress and moisture gradient. To improve the drying quality and dimensional stability of bamboo stems, this study applied hygrothermal treatment under atmospheric pressure to newly felled bamboo stems. The temperature, relative humidity, and duration of the treatment were optimized using an orthogonal L₉ (3⁴) experimental design. The results show that the surface color of round bamboo became more uniform after hygrothermal treatment. Furthermore, hygrothermal treatment could reduce the cuticular wax and silicon layer detachment on the surface of round bamboo after drying. According to the range and variance analysis, the relative humidity had the greatest impact on dimensional stability, followed by treatment duration, whereas the temperature had a limited effect. The swelling rate of round bamboo under a hygrothermal treatment at a relative humidity of 95%, a temperature of 95 °C, and a duration of 3 h was decreased 53.72% and 62.76% compared with untreated round bamboo after moisture or water absorption for 7 d, respectively. However, no significant difference was observed in the color of the round bamboo under different hygrothermal treatment conditions. Overall, this study suggests that hygrothermal treatment could be a highly promising technology for improving the dimensional stability of newly felled bamboo stems. Full article

Weeds are a significant adverse factor affecting rice growth, and their efficient removal necessitates an accurate, efficient, and well-generalizing weed detection method. However, weed detection faces challenges such as a complex vegetation environment, the similar morphology and color of weeds, and crops and varying lighting conditions. The current research has yet to address these issues adequately. Therefore, we propose GE-YOLO to identify three common types of weeds in rice fields in the Hunan province of China and to validate its generalization performance. GE-YOLO is an improvement based on the YOLOv8 baseline model. It introduces the Neck network with the Gold-YOLO feature aggregation and distribution network to enhance the network’s ability to fuse multi-scale features and detect weeds of different sizes. Additionally, an EMA attention mechanism is used to better learn weed feature representations, while a GIOU loss function provides smoother gradients and reduces computational complexity. Multiple experiments demonstrate that GE-YOLO achieves 93.1% mAP, 90.3% F1 Score, and 85.9 FPS, surpassing almost all mainstream object detection algorithms such as YOLOv8, YOLOv10, and YOLOv11 in terms of detection accuracy and overall performance. Furthermore, the detection results under different lighting conditions consistently maintained a high level above 90% mAP, and under conditions of heavy occlusion, the average mAP for all weed types reached 88.7%. These results indicate that GE-YOLO has excellent detection accuracy and generalization performance, highlighting the potential of GE-YOLO as a valuable tool for enhancing weed management practices in rice cultivation. Full article