Search Results (179)

The quality and freshness of fruits and vegetables are critical factors in consumer acceptance and are significantly affected during transport and storage. This study aimed to evaluate the quality of greenhouse-grown tomatoes stored for 24 days by combining non-destructive image analysis, spectrophotometric assays (including total phenolic content and antioxidant and antiradical activity assessments), and attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy. Additionally, water activity, moisture content, total soluble solids, texture, and color were evaluated. Most physicochemical changes occurred between days 14 and 17, without major impact on overall fruit quality. A progressive transition in peel hue from orange to dark orange, and increased surface irregularity of their textural image were noted. Moreover, the combined use of instrumental and image analyses results via multivariate analysis allowed the clear discrimination of tomatoes according to storage days. In this sense, tomato samples were effectively classified by ATR-FTIR spectral bands, linked to carotenoids, phenolics, and polysaccharides. Machine learning (ML) models, including Random Forest and Gradient Boosting, were trained on image-derived features and accurately predicted shelf life and quality traits, achieving R² values exceeding 0.9. The findings demonstrate the effectiveness of combining imaging, spectroscopy, and ML for non-invasive tomato quality monitoring and support the development of predictive tools to improve postharvest handling and reduce food waste. Full article

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

Understanding and mapping ephemeral gullies (EGs) are vital for enhancing agricultural productivity and achieving food security. This study proposes an upscaling-based strategy to refine the predictive mapping of EGs, utilizing high-resolution Pléiades Neo (0.6 m) and medium-resolution Sentinel-2 (10 m) satellite imagery, alongside ground-truth EGs mapping in Niagara Region, Canada. The research involved generating spectral feature maps using Blue, Green, Red, and Near-infrared spectral bands, complemented by indices indicative of surface wetness, vegetation, color, and soil texture. Employing the Random Forest (RF) algorithm, this study executed three distinct strategies for EGs identification. The first strategy involved direct calibration using Sentinel-2 spectral features for 10 m resolution mapping. The second strategy utilized high-resolution Pléiades Neo data for model calibration, enabling EGs mapping at resolutions of 0.6, 2, 4, 6, and 8 m. The third, or upscaling strategy, applied the high-resolution calibrated model to medium-resolution Sentinel-2 imagery, producing 10 m resolution EGs maps. The accuracy of these maps was evaluated against actual data and compared across strategies. The findings highlight the Variable Importance Measure (VIM) of different spectral features in EGs identification, with normalized near-infrared (Norm NIR) and normalized red reflectance (Norm Red) exhibiting the highest and lowest VIM, respectively. Vegetation-related indices demonstrated a higher VIM compared to surface wetness indices. The overall classification error of the upscaling strategy at spatial resolutions of 0.6, 2, 4, 6, 8, and 10 m (Upscaled), as well as that of the direct Sentinel-2 model, were 7.9%, 8.2%, 9.1%, 10.3%, 11.2%, 12.5%, and 14.5%, respectively. The errors for EGs maps at various resolutions revealed an increase in identification error with higher spatial resolution. However, the upscaling strategy significantly improved the accuracy of EGs identification in medium spatial resolution scenarios. This study not only advances the methodology for EGs mapping but also contributes to the broader field of precision agriculture and environmental management. By providing a scalable and accessible approach to EGs mapping, this research supports enhanced soil conservation practices and sustainable land management, addressing key challenges in agricultural sustainability and environmental stewardship. Full article

Rising morbidity and mortality from cardiovascular disease (CVD) have increased interest in precision and preventive management to reduce long-term sequelae. While retinal imaging has traditionally been recognized for identifying vascular changes in systemic conditions such as hypertension and type 2 diabetes mellitus, a new ophthalmologic field, cardiac-oculomics, has associated retinal biomarker changes with other cardiovascular diseases with retinal manifestations. Several imaging modalities visualize the retina, including color fundus photography (CFP), optical coherence tomography (OCT), and OCT angiography (OCTA), which visualize the retinal surface, the individual retinal layers, and the microvasculature within those layers, respectively. In these modalities, imaging-derived biomarkers can present due to CVD and have been linked to the presence, progression, or risk of developing a range of CVD, including hypertension, carotid artery disease, valvular heart disease, cerebral infarction, atrial fibrillation, and heart failure. Promising artificial intelligence (AI) models have been developed to complement existing risk-prediction tools, but standardization and clinical trials are needed for clinical adoption. Beyond risk estimation, there is growing interest in assessing real-time cardiovascular status to track vascular changes following pharmacotherapy, surgery, or acute decompensation. This review offers an up-to-date assessment of the cardiac-oculomics literature and aims to raise awareness among cardiologists and encourage interdepartmental collaboration. Full article

This work introduces the use of Bayesian Neural Networks (BNNs) for inferring chlorophyll-a concentration ([CHL-a]) from remotely sensed data. BNNs are probabilistic models that associate a probability distribution to the neural network parameters and rely on Bayes’ rule for training. The performance of the proposed probabilistic model is compared to that of standard ocean color algorithms, namely ocean color 4 (OC4) and ocean color index (OCI). An extensive in situ bio-optical dataset was used to train and validate the ocean color models. In contrast to established methods, the BNN allows for enhanced modeling flexibility, where different variables that affect phytoplankton phenology or describe the state of the ocean can be used as additional input for enhanced performance. Our results suggest that BNNs perform at least as well as established methods, and they could achieve 20–40% lower mean squared errors when additional input variables are included, such as the sea surface temperature and its climatological mean alongside the coordinates of the prediction. The BNNs offer means for uncertainty quantification by estimating the probability distribution of [CHL-a], building confidence in the [CHL-a] predictions through the variance of the predictions. Furthermore, the output probability distribution can be used for risk assessment and decision making through analyzing the quantiles and shape of the predicted distribution. Full article

This study introduces an innovative and scalable approach for automated road surface assessment by integrating Mobile Mapping System (MMS)-based LiDAR data analysis with an open-source WebGIS platform. In a U.S.-based case study, over 20 datasets were collected along Interstate I-65 in West Lafayette, Indiana, using the Purdue Wheel-based Mobile Mapping System—Ultra High Accuracy (PWMMS-UHA), following Indiana Department of Transportation (INDOT) guidelines. Preprocessing included noise removal, resolution reduction to 2 cm, and ground/non-ground separation using the Cloth Simulation Filter (CSF), resulting in Bare Earth (BE), Digital Terrain Model (DTM), and Above Ground (AG) point clouds. The optimized BE layer, enriched with intensity and color information, enabled crack detection through Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Forest (RF) classification, with and without intensity normalization. DBSCAN parameter tuning was guided by silhouette scores, while model performance was evaluated using precision, recall, F1-score, and the Jaccard Index, benchmarked against reference data. Results demonstrate that RF consistently outperformed DBSCAN, particularly under intensity normalization, achieving Jaccard Index values of 94% for longitudinal and 88% for transverse cracks. A key contribution of this work is the integration of geospatial analytics into an interactive, open-source WebGIS environment—developed using Blender, QGIS, and Lizmap—to support predictive maintenance planning. Moreover, intervention thresholds were defined based on crack surface area, aligned with the Pavement Condition Index (PCI) and FHWA standards, offering a data-driven framework for infrastructure monitoring. This study emphasizes the practical advantages of comparing clustering and machine learning techniques on 3D LiDAR point clouds, both with and without intensity normalization, and proposes a replicable, computationally efficient alternative to deep learning methods, which often require extensive training datasets and high computational resources. Full article

An accurate 3D pose estimation of spherical objects remains challenging in industrial inspections and robotics due to their geometric symmetries and limited feature discriminability. This study proposes a texture-optimized simulation framework to enhance pose prediction accuracy through optimizing the surface texture features of the design samples. A hierarchical texture design strategy was developed, incorporating complexity gradients (low to high) and color contrast principles, and implemented via VTK-based 3D modeling with automated Euler angle annotations. The framework generated 2297 synthetic images across six texture variants, which were used to train a MobileNet model. The validation tests demonstrated that the high-complexity color textures achieved superior performance, reducing the mean absolute pose error by 64.8% compared to the low-complexity designs. While color improved the validation accuracy universally, the test set analyses revealed its dual role: complex textures leveraged chromatic contrast for robustness, whereas simple textures suffered color-induced noise (a 35.5% error increase). These findings establish texture complexity and color complementarity as critical design criteria for synthetic datasets, offering a scalable solution for vision-based pose estimation. Physical experiments confirmed the practical feasibility, yielding 2.7–3.3° mean errors. This work bridges the simulation-to-reality gaps in symmetric object localization, with implications for robotic manipulation and industrial metrology, while highlighting the need for material-aware texture adaptations in future research. Full article

Background: Lithium disilicate ceramic veneers are considered the gold standard in aesthetic dentistry due to their translucency, strength, and adhesive bonding properties. This clinical case report details the aesthetic rehabilitation of a patient through the use of pressed lithium disilicate veneers, highlighting the treatment workflow, material selection rationale, and the long-term functional and aesthetic outcomes achieved. Methods: A review was conducted to evaluate the long-term success of lithium disilicate. A case study is presented that involves a 32-year-old female patient with anterior tooth discoloration, minor morphological discrepancies, and a desire for smile enhancement. A conservative approach using pressed lithium disilicate was chosen to restore harmony and enhance natural aesthetics. The treatment involved minimally invasive tooth preparation, digital smile design, and adhesive cementation using a total-etch technique with light-cured resin cement. High-resolution intra-oral and extra-oral photographs documented the case, capturing the preoperative, preparation, and final restoration stages. These images highlight shade matching, margin adaptation, and smile transformation after veneering. Results: Postoperative evaluation showed excellent aesthetic outcomes, color integration, and marginal adaptation, with the patient expressing high satisfaction. The veneers exhibited optimal translucency and strength, ensuring long-term durability. A one-year follow-up revealed no debonding, marginal discoloration, or surface degradation, confirming the clinical reliability of lithium disilicate veneers. Conclusions: Lithium disilicate provides predictability, durability, and high aesthetic results, making it an ideal choice for minimally invasive smile enhancement. The use of photographic documentation emphasizes the importance of case planning, precise preparation, and adhesive bonding for successful outcomes. Future research should focus on long-term survival rates and complication prevention to further refine material selection and bonding protocols. Full article

LIDAR technology is widely used in autonomous driving and environmental sensing, but its accuracy is significantly affected by variations in vehicle surface reflectivity. This study models and predicts the impact of different LIDAR sensor specifications and vehicle surface paints on laser intensity measurements. Laser intensity data from the experiments of Shung et al. were analyzed alongside vehicle color, angle, and distance. Multiple machine learning models were tested, with Gaussian Process Regression (GPR) performing best (RMSE = 0.87451, R² = 0.99924). To enhance the model’s physical interpretability, laser intensity values were correlated with LIDAR optical power equations, and curve fitting was applied to refine the relationship. The model was validated using the input parameters from Shung et al.’s experiments, comparing predicted intensity values with reference measurements. The results show that the model achieves an overall accuracy of 99% and is successful in laser intensity prediction. To assess real-world performance, the model was tested on the CUPAC dataset, which includes various traffic and weather conditions. Spatial filtering was applied to isolate laser intensities reflected only from the vehicle surface. The highest accuracy, 98.891%, was achieved for the SW-Gloss (White) surface, while the lowest accuracy, 98.195%, was recorded for the SB-Matte (Black) surface. The results confirm that the model effectively predicts laser intensity across different surface reflectivity conditions and remains robust across different channels LIDAR systems. Full article

A method for apple phenotypic feature extraction and growth anomaly identification based on deep learning and natural language processing technologies is proposed in this paper, aiming to enhance the accuracy of apple quality detection and anomaly prediction in agricultural production. This method integrates instance segmentation, edge perception mechanisms, attention mechanisms, and multimodal data fusion to accurately extract an apple’s phenotypic features, such as its shape, color, and surface condition, while identifying potential anomalies which may arise during the growth process. Specifically, the edge transformer segmentation network is employed to combine deep convolutional networks (CNNs) with the Transformer architecture, enhancing feature extraction and modeling long-range dependencies across different regions of an image. The edge perception mechanism improves segmentation accuracy by focusing on the boundary regions of the apple, particularly in the case of complex shapes or surface damage. Additionally, the natural language processing (NLP) module analyzes agricultural domain knowledge, such as planting records and meteorological data, providing insights into potential causes of growth anomalies and enabling more accurate predictions. The experimental results demonstrate that the proposed method significantly outperformed traditional models across multiple metrics. Specifically, in the apple phenotypic feature extraction task, the model achieved exceptional performance, with accuracy of 0.95, recall of 0.91, precision of 0.93, and mean intersection over union (mIoU) of 0.92. Furthermore, in the growth anomaly identification task, the model also performed excellently, with a precision of 0.93, recall of 0.90, accuracy of 0.91, and mIoU of 0.89, further validating its efficiency and robustness in handling complex growth anomaly scenarios. The method’s integration of image data with agricultural knowledge provides a comprehensive approach to both apple quality detection and growth anomaly prediction, offering reliable decision support for agricultural production. The proposed method, by integrating image data with agricultural domain knowledge, provides precise decision support for agricultural production, not only improving the efficiency and accuracy of apple quality detection but also offering reliable technical assurance for agricultural economic analysis. Full article

This study evaluated the efficiency of methylene blue (MB) removal by using activated carbon produced from olive pits. The activated carbon (OPAC) was characterized by scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and Brunauer–Emmett–Teller (BET). The adsorption process was optimized in two stages using factorial design. Based on the existing literature, the first stage selected the most influential variables (reaction time, dosage, pH, and dye concentration). Response surface methodology (RSM) and artificial neural network (ANN) approaches have been combined to optimize and model the adsorption of MB. To assess the optimal conditions for MB adsorption, RSM was initially applied using four controllable operating parameters. Throughout the optimization process, various independent variables were employed, including initial dye concentrations ranging from 25 to 125 mg/L, adsorbent dosages ranging from 0.1 to 0.9 g/L, pH values spanning from 1 to 9, and contact times ranging from 15 to 75 min. Moreover, the R² value (R² = 0.9804) indicates that regression can effectively forecast the response of the adsorption process within the examined range. Thermodynamic studies were performed for three different temperatures between 293 and 303 K. Isothermal analysis parameters and negative Gibbs free energy indicate that the process is spontaneous and favorable. The data best fit the Langmuir model. This research showcases the effectiveness of optimizing and predicting the color removal process through the combined RSM-ANN approach. It highlights the effectiveness of adsorption using OPAC as a viable primary treatment method for the removal of color from wastewater-containing dyes. Full article

This study assesses the relationship between the color changes and physicochemical properties of thermally treated biomass feedstocks, using colorimetric measurements to study the color difference values of straw, forest, and grass undergoing thermal treatments at 120~200 °C. We establish a multiple linear regression model to correlate the physicochemical properties of the treated solid products with three-dimensional color coordinates (L*, a*, b*). The results indicate that as the treatment temperature increases, the color difference ($∆E_{ab}^{\mathrm{*}}$) value also increases. Meanwhile, the number of conjugated structures in the chromophore groups increases, causing the color of the solid products to tend toward black. The ash, volatile, fixed carbon, carbon, hydrogen, oxygen, nitrogen, higher calorific value, solid yield, energy yield, bulk density, and water contact angle of the thermally treated biomass feedstocks have a statistical measure of R² ≥ 0.90 with the three-dimensional color coordinates, indicating a good correlation. Therefore, it is possible to quickly predict the basic physicochemical properties of thermally treated biomass feedstocks based on their surface color, providing a theoretical basis for the rapid quality assessment of solid products using CIELAB color changes in industrial applications. Full article

The processing of beans begins with a particularly time-consuming procedure, the hydration of the seeds. Ultrasonic treatment (US) represents a potential environmentally friendly method for process acceleration, while near-infrared spectroscopy (NIR) is a proposedly suitable non-invasive monitoring tool to assess compositional changes. Our aim was to examine the hydration process of red kidney beans of varying sizes and origins. Despite the varying surface areas, the beans’ soaking times of 13–15, 15–17, and 17–19 mm did not reveal significant differences between any of the groups (control; low power: 180 W, 20 kHz; high power: 300 W, 40 kHz). US treatment was observed to result in the release of greater quantities of water-soluble components from the beans. This was evidenced by the darkening of the soaking water’s color, the increase in the a* color parameter, and the rise in the dry matter value. NIRs, in combination with chemometric tools, are an effective tool for predicting the characteristics of bean-soaking water. The PLSR- and SVR-based modelling for dry matter content and light color parameters demonstrated robust model fits with cross and test set-validated R² values (>0.95), suggesting that these techniques can effectively capture the chemical information of the samples. Full article

Recently, laser-induced coloring of metal surfaces has emerged as a hot topic in the field of color manufacturing. In existing research, we have not been able to find a reliable method to swiftly acquire all the color ranges achievable with current materials. This limitation hinders further research and application of laser-induced metal coloring, making it challenging to scientifically and effectively reproduce colors in images. In this study, we introduced a genetic algorithm tailored for predicting the maximization of color gamut area. By employing an elitist strategy for genetic selection and predicting the maximum color gamut among a multi-objective optimization parameter population, we successfully explored the color gamut of stainless steel. The color gamut $S$ converged to 0.0022, offering a rapid and efficient approach for color gamut exploration. Building on this, we devised a comprehensive image color reproduction process and developed an image color gamut mapping toolkit and an image vectorization toolkit. These tools are designed for color separation, color gamut mapping, and vectorization of target images, enabling successful color reproduction through laser-induced coloring. Additionally, we conducted a color difference analysis experiment using 2 mm 304 stainless steel, demonstrating that material thickness can mitigate color cast issues in laser-induced coloring. The color difference ($\mathsf{\Delta }E$) values in the color reproduction experiment were 2.18, 2.97, and 2.72, respectively, indicating the reliability of image color reproduction on stainless steel surfaces. This research addresses the challenge of color gamut exploration in laser-induced coloring, presenting a novel solution for color reproduction via laser-induced coloring on metal surfaces, and holds promising applications. Full article

Imitation precious wood materials have become a research focus due to their good quality, high safety level, excellent performance, rich color, varied textures, and high utilization rates. However, their uneven dyeing, poor color stability, and lack of durability limit their further application. This study utilized a neural network model optimized with the Gray Wolf Algorithm (GWA) for color matching, using acidic dyes as raw materials and deep eutectic solvents (DESs) for modification. Functional reagents like nano tungsten trioxide (WO₃) and titanium dioxide (TiO₂) were introduced alongside polyvinyl alcohol (PVA) as a modifier. A dyeing-enhancement modification process was employed to create a poplar veneer that exhibited uniform and stable color performance with a smooth surface, mimicking that of precious wood. Computerized color matching was used to adjust the dye formulation for staining, ensuring stable colorimetric values on the veneer surface, which closely resembled natural precious wood. The average mean squared error in dye concentration prediction, after processing with the Gray Wolf Algorithm and a basic neural network algorithm, decreased from 0.13 to 0.006, ensuring repeatability and consistency in wood dyeing. Analysis and characterization using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and permeability testing revealed that nano TiO₂ and WO₃ particles were uniformly distributed within the wood cell lumens and firmly bonded. Mechanical testing on PVA-glued veneers showed that compared to untreated poplar veneers, the tensile strength of the imitation wood increased by approximately 62.5%, and the bending strength reached 809.09 MPa, significantly improving the flexibility and tensile properties of the poplar veneer. This study is the first to adopt a DES-modified dyeing-enhancement modification process to improve the dyeing performance, uniformity, durability, and structural stability of wood, showcasing its great potential in architectural decoration, high-end furniture, and artisanal crafts. Full article