Search Results (103)

Floods stand as one of the most harmful natural disasters, which have become more dangerous because of climate change effects on urban structures and agricultural fields. This research presents a comprehensive flood mapping approach that combines multi-sensor satellite data with a machine learning method to evaluate the July 2021 flood in the Netherlands. The research developed 25 different feature scenarios through the combination of Sentinel-1, Landsat-8, and Radarsat-2 imagery data by using backscattering coefficients together with optical Normalized Difference Water Index (NDWI) and Hue, Saturation, and Value (HSV) images and Synthetic Aperture Radar (SAR)-derived Grey Level Co-occurrence Matrix (GLCM) texture features. The Random Forest (RF) classifier was optimized before its application based on two different flood-prone regions, which included Zutphen’s urban area and Heijen’s agricultural land. Results demonstrated that the multi-sensor fusion scenarios (S18, S20, and S25) achieved the highest classification performance, with overall accuracy reaching 96.4% (Kappa = 0.906–0.949) in Zutphen and 87.5% (Kappa = 0.754–0.833) in Heijen. For the flood class F1 scores of all scenarios, they varied from 0.742 to 0.969 in Zutphen and from 0.626 to 0.969 in Heijen. Eventually, the addition of SAR texture metrics enhanced flood boundary identification throughout both urban and agricultural settings. Radarsat-2 provided limited benefits to the overall results, since Sentinel-1 and Landsat-8 data proved more effective despite being freely available. This study demonstrates that using SAR and optical features together with texture information creates a powerful and expandable flood mapping system, and RF classification performs well in diverse landscape settings. Full article

Real-time wildlife monitoring on edge devices poses significant challenges due to limited power, constrained bandwidth, and unreliable connectivity, especially in remote natural habitats. Conventional object detection systems often transmit redundant data of the same animals detected across multiple consecutive frames as a part of a single event, resulting in increased power consumption and inefficient bandwidth usage. Furthermore, maintaining consistent animal identities in the wild is difficult due to occlusions, variable lighting, and complex environments. In this study, we propose a lightweight hybrid tracking framework built on the YOLOv8m deep neural network, combining motion-based Kalman filtering with Local Binary Pattern (LBP) similarity for appearance-based re-identification using texture and color features. To handle ambiguous cases, we further incorporate Hue-Saturation-Value (HSV) color space similarity. This approach enhances identity consistency across frames while reducing redundant transmissions. The framework is optimized for real-time deployment on edge platforms such as NVIDIA Jetson Orin Nano and Raspberry Pi 5. We evaluate our method against state-of-the-art trackers using event-based metrics such as MOTA, HOTA, and IDF1, with a focus on detected animals occlusion handling, trajectory analysis, and counting during both day and night. Our approach significantly enhances tracking robustness, reduces ID switches, and provides more accurate detection and counting compared to existing methods. When transmitting time-series data and detected frames, it achieves up to 99.87% bandwidth savings and 99.67% power reduction, making it highly suitable for edge-based wildlife monitoring in resource-constrained environments. Full article

Underwater image enhancement (UIE) technology plays a vital role in marine resource exploration, environmental monitoring, and underwater archaeology. However, due to the absorption and scattering of light in underwater environments, images often suffer from blurred details, color distortion, and low contrast, which seriously affect the usability of underwater images. To address the above limitations, a lightweight transformer-based model (LITM) is proposed for improving underwater degraded images. Firstly, our proposed method utilizes a lightweight RGB transformer enhancer (LRTE) that uses efficient channel attention blocks to capture local detail features in the RGB domain. Subsequently, a lightweight HSV transformer encoder (LHTE) is utilized to extract global brightness, color, and saturation from the hue–saturation–value (HSV) domain. Finally, we propose a multi-modal integration block (MMIB) to effectively fuse enhanced information from the RGB and HSV pathways, as well as the input image. Our proposed LITM method significantly outperforms state-of-the-art methods, achieving a peak signal-to-noise ratio (PSNR) of 26.70 and a structural similarity index (SSIM) of 0.9405 on the LSUI dataset. Furthermore, the designed method also exhibits good generality and adaptability on unpaired datasets. Full article

Paulownia elongata wood is characterized by rapid mass gain, but its limited mechanical strength hinders engineering applications. This study aimed to determine the effect of thermal modification in a steam atmosphere (at temperatures of 180 °C and 190 °C for 12 or 6 h with 3 or 6 h of steam dosing) on wood’s selected physicochemical and aesthetic properties. Color changes (CIELAB), chemical composition (FTIR), density, and compressive strength parallel to the grain were evaluated. The results showed a clear darkening of the wood, a shift in hues towards red and yellow, and an increase in color saturation depending on the treatment parameters. FTIR spectroscopy confirmed a reduction in hydroxyl and carbonyl groups, indicating thermal degradation of hemicelluloses and extractives. Wood density remained relatively stable, despite observed mass losses and reduced swelling. The most significant increase in compressive strength, reaching 27%, was achieved after 6 h of modification at 180 °C with a concurrent 6 h steam dosing time. The obtained results confirm that thermal treatment can effectively improve the functional and visual properties of paulownia wood, favoring its broader application in the furniture and construction industries. Full article

When a CMOS (Complementary Metal–Oxide–Semiconductor) imaging system operates at a high frame rate or a high line rate, the exposure time of the imaging system is limited, and the acquired image data will be dark, with a low signal-to-noise ratio and unsatisfactory sharpness. Therefore, in order to improve the visibility and signal-to-noise ratio of remote sensing images based on CMOS imaging systems, this paper proposes a low-light remote sensing image enhancement method and a corresponding ZYNQ (Zynq-7000 All Programmable SoC) design scheme called the BGIR (Bilateral-Guided Image Restoration) algorithm, which uses an improved multi-scale Retinex algorithm in the HSV (hue–saturation–value) color space. First, the RGB image is used to separate the original image’s H, S, and V components. Then, the V component is processed using the improved algorithm based on bilateral filtering. The image is then adjusted using the gamma correction algorithm to make preliminary adjustments to the brightness and contrast of the whole image, and the S component is processed using segmented linear enhancement to obtain the base layer. The algorithm is also deployed to ZYNQ using ARM + FPGA software synergy, reasonably allocating each algorithm module and accelerating the algorithm by using a lookup table and constructing a pipeline. The experimental results show that the proposed method improves processing speed by nearly 30 times while maintaining the recovery effect, which has the advantages of fast processing speed, miniaturization, embeddability, and portability. Following the end-to-end deployment, the processing speeds for resolutions of 640 × 480 and 1280 × 720 are shown to reach 80 fps and 30 fps, respectively, thereby satisfying the performance requirements of the imaging system. Full article

Disaster risk reduction (DRR) education is considered increasingly necessary, particularly for children. DRR educational interventions aim to enhance knowledge and attitudes related to self-protective capacity. However, comparative studies on students in areas prone to different disasters and comprehensive criteria covering both knowledge and attitudes toward behavior remain limited. A short DRR course was developed for primary schools across three regions (mountainous, low-lying, and coastal) in Hue City, one of Vietnam’s most vulnerable areas to extreme weather events. This study aimed to comprehensively evaluate student performance by applying Bloom’s taxonomy and treatment-control pre-post-follow-up design with panel analysis methods. From December 2022 to September 2023, three surveys, involving 517 students each, were conducted in six schools (three schools received the course and surveys, while the other three only participated in surveys). The intervention revealed similarities and differences between the groups. The course positively impacted on some elements of knowledge and preparedness intentions in students from low-lying and mountainous regions (including ethnic minorities). Higher-grade students in the mountainous region showed improvement in intentions, but not in attitudes toward self-protection. No gender differences in intentions were found. Although limited overall improvements, the study’s various methods, approaches and continuous assessment can be applied globally to design, implement, and assess DRR education courses effectively. Full article

The removal of partially hydrogenated fats, as well as the substitution of saturated fats with healthier alternatives, has become increasingly common due to their well-established association with adverse health effects. As a result, the demand for alternative formulations in the food industry has driven the development of a promising emerging technology: oleogels. Oleogels are a semi-solid material made by trapping liquid oil within a three-dimensional network formed by structuring agents. Within this context, this study aimed to develop and characterize margarines prepared with oleogels formulated from extra virgin olive oil, coconut oil, starch, and beeswax at varying concentrations. The proposed oleogel-based formulations exhibited a high melting temperature range and lower enthalpy. Although lipid oxidation levels differed between the commercial and oleogel-based margarines, they remained within acceptable limits. A significant difference in color was observed, with the oleogel formulations imparting a slight greenish hue compared to the commercial margarine. In terms of microstructure, the commercial margarine presented smaller and more uniformly distributed water droplets. Oleogel-based margarines demonstrated technological feasibility. Considering consumers’ growing interest in food innovation and health-conscious products, olive oil-based oleogel margarines represent a promising alternative, particularly due to the nutritional benefits associated with olive oil. Full article

Visual navigation technology holds significant potential for applications involving unmanned aerial vehicles (UAVs). However, the inherent spectral limitations of optical-dependent navigation systems prove particularly inadequate for high-altitude long-endurance (HALE) UAV operations, as they are fundamentally constrained in maintaining reliable environment perception under conditions of fluctuating illumination and persistent cloud cover. To address this challenge, this paper introduces microwave vision to assist optical vision for environmental measurement and proposes a novel microwave vision-enhanced environmental perception method. In particular, the richness of perceived environmental information can be enhanced by SAR and optical image fusion processing in the case of sufficient light and clear weather. In order to simultaneously mitigate inherent SAR speckle noise and address existing fusion algorithms’ inadequate consideration of UAV navigation-specific environmental perception requirements, this paper designs a SAR Target-Augmented Fusion (STAF) algorithm based on the target detection of SAR images. On the basis of image preprocessing, this algorithm utilizes constant false alarm rate (CFAR) detection along with morphological operations to extract critical target information from SAR images. Subsequently, the intensity–hue–saturation (IHS) transform is employed to integrate this extracted information into the optical image. The experimental results show that the proposed microwave vision-enhanced environmental perception method effectively utilizes microwave vision to shape target information perception in the electromagnetic spectrum and enhance the information content of environmental measurement results. The unique information extracted by the STAF algorithm from SAR images can effectively enhance the optical images while retaining their main attributes. This method can effectively enhance the environmental measurement robustness and information acquisition ability of the visual navigation system. Full article

Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome these difficulties, the study integrated computer vision techniques with UAV-based remote sensing data to establish a rapid and non-invasive method for estimating the LAI in maize. Multispectral imagery of maize was acquired via UAV platforms across various phenological stages, and vegetation features were derived based on the Excess Green (ExG) Index and the Hue–Saturation–Value (HSV) color space. LAI standardization was performed through edge detection and the cumulative distribution function. The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R² values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively. When HSV-based optimization was applied, VisLAI achieved even better performance, with R² values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages. The estimation results were validated against ground-truth data collected using the LAI-2200C plant canopy analyzer and compared with six machine learning algorithms, including Gradient Boosting (GB), Random Forest (RF), Ridge Regression (RR), Support Vector Regression (SVR), and Linear Regression (LR). Among these, GB achieved the best performance, with R² values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34. However, VisLAI consistently outperformed all machine learning models, especially during the grain filling stage, demonstrating superior robustness and accuracy. The VisLAI model proposed in this study effectively utilizes UAV-captured visible light imagery and computer vision techniques to achieve accurate, efficient, and non-destructive estimation of maize LAI. It outperforms traditional and machine learning-based approaches and provides a reliable solution for real-world maize growth monitoring and agricultural decision-making. Full article

Extensive cattle farming significantly contributes to Greece’s agricultural economy. In such systems, animals mainly graze on natural grasslands whose biodiversity significantly affects meat quality traits. In Greece, the sector faces several economic challenges, while the literature investigating beef quality produced by these systems is scarce. Hence, this study aimed to (i) evaluate farms’ economic performance; (ii) assess meat quality; and (iii) investigate the presence of heavy metals in liver samples of extensively reared beef cattle. The study involved three farms located in the Axios River Delta, a protected area of significant ecological importance in Northern Greece. A designated questionnaire was used to collect farm technical (herd size, meat production, grazing, feeding, reproduction, animal health) and economic data (income, variable costs). Meat samples of the Longissimus dorsi muscle (ninth rib) from 54 carcasses were collected and subjected to physicochemical (color, pH, texture, chemical composition, fatty acid profile) and microbiological analyses. Additionally, heavy metal analysis was conducted on 14 liver samples. A comparative analysis using parametric and non-parametric tests was performed to assess differences in meat quality traits between the 1st and 15th days of storage. The economic analysis showed that all studied farms operated with losses, with the average gross margin excluding subsidies being negative at EUR 130.5 ± 92.60/year per animal. Beef exhibited low fat content (1.1 ± 1.12%), with an average pH₂₄ value of 5.5 ± 0.36, respectively. The concentrations of polyunsaturated, monounsaturated, and saturated fatty acids were 2.7 ± 0.72%, 44.6 ± 4.71%, and 47.3 ± 4.91%, respectively. Over the 15-day storage period, the yellowness (b*) value (p < 0.01), hue angle (p < 0.001), cohesiveness (p < 0.01), and springiness (p < 0.01) significantly decreased, while the lightness (L*) value significantly increased (p < 0.01). The mean Total Mesophilic Viable Counts and Total Enterobacterales were 5.0 log₁₀ CFU/g and 2.34 log₁₀ CFU/g, respectively, while heavy metal concentrations in bovine livers were below the maximum limits set by the European Commission. The results suggest that, despite the financial losses observed, beef’s improved color parameters during storage, along with other favorable quality traits, highlight the potential of extensive cattle farming to meet consumer demand and support value-added marketing. Full article

The “yellow-skinned” Nanhong agate represents a unique variety of Nanhong agate found in northeastern Yunnan, China, and it is highly valued for its distinctive yellow exterior and clear red–yellow interface. Owing to the limited research on this variety, the present study provides the first comprehensive analysis. Field surveys and various laboratory techniques—including polarizing microscopy, scanning electron microscopy (SEM), Fourier-transform infrared (FTIR) spectrometry, ultraviolet–visible (UV-VIS) absorption spectrometry, Raman spectroscopy, micro X-ray diffraction (µ-XRD) with Rietveld refinement, electron microprobe analysis (EPMA), and laser ablation–inductively coupled plasma mass spectrometry (LA-ICP-MS)—were utilized to investigate its gemological, microtextural, spectroscopic, and geochemical characteristics. Field surveys identified the occurrence states of the “yellow-skinned” Nanhong agate. The laboratory results indicate that the agate primarily consists of α-quartz, with minor amounts of moganite, goethite, and hematite. The coloring mechanism observed in this study is consistent with the findings of previous studies: the external yellow coloration is due to goethite, while the internal red hue is attributed to hematite. Its unique pseudo-granular silica (Type III) structure provides a foundational basis for the later formation of the “yellow-skinned” agate variety, and geochemical data reveal the distribution patterns of elements. Based on geological surveys and experimental data, the formation of the “yellow-skinned” Nanhong agate in northeastern Yunnan can be divided into two stages: first, hydrothermal fluids filled the vesicles in the Permian Emeishan Basalt Formation (P₂β), leading to the formation of primary Nanhong agate. Subsequently, the Type III primary agate underwent weathering, erosion, transport, and deposition in the red–brown sandy mudstone of the Lower Triassic Feixianguan Formation (T₁f). The sedimentary environment in the second stage facilitated the conversion of outer hematite into goethite, resulting in the distinct “yellow-skinned” appearance with a clear red–yellow boundary. Based on the occurrence and stratigraphic relations, this study constrains the formation age of the “yellow-skinned” Nanhong agate to approximately 261.6 Ma. Full article

This study presents a novel approach for the detection and severity assessment of pest-induced damage in maize plants, focusing on the Zyginidia pullula pest. A newly developed dataset is utilized, where maize plant images are initially classified into two primary categories: healthy and infected. Subsequently, infected samples are categorized into three distinct severity levels: low, medium, and high. Both traditional and deep learning-based feature extraction techniques are employed to achieve this. Specifically, hand-crafted feature extraction methods, including Gabor filters, Gray Level Co-occurrence Matrix, and Hue-Saturation-Value color space, are combined with CNN-based models such as ResNet-50, DenseNet-201, and EfficientNet-B2. The maize images undergo preprocessing and segmentation using Contrast Limited Adaptive Histogram Equalization and U2Net, respectively. Extracted features are then fused and subjected to Principal Component Analysis for dimensionality reduction. The classification task is performed using Support Vector Machines, Random Forest, and Artificial Neural Networks, ensuring robust and accurate detection. The experimental results demonstrate that the proposed hybrid approach outperforms individual feature extraction methods, achieving a classification accuracy of up to 92.55%. Furthermore, integrating multiple feature representations significantly enhances the model’s ability to differentiate between varying levels of pest damage. Unlike previous studies that primarily focus on plant disease detection, this research uniquely addresses the quantification of pest-induced damage, offering a valuable tool for precision agriculture. The findings of this study contribute to the development of automated, scalable, and efficient pest monitoring systems, which are crucial for minimizing yield losses and improving agricultural sustainability. Full article

Almond peel extracts, containing 0.2–0.8% (w/w) phenolic compounds with notable antioxidant and antimicrobial activities, could be used as a natural source of active compounds for the development of active films for food preservation. In this study, almond peel extracts obtained by subcritical water extraction at 160 and 180 °C were incorporated into PLA films (PLA-E160 and PLA-E180). The films were characterized in terms of their microstructure, mechanical, barrier, optical and thermal properties. Furthermore, the release of phenolic compounds and hydroximethylfurfural (HFM) into food simulants with different polarity was evaluated, as well as the film’s potential antioxidant and antimicrobial activities. To validate their effectiveness as active packaging materials, shelf-life studies were conducted on fresh orange juice and sunflower oil packaged using PLA-160 films. The results show that the incorporation of the almond peel extracts led to significant changes in the films’ microstructure and mechanical properties, which became darker, mechanically less resistant, and stretchable (p < 0.05), with slightly lower thermal stability than neat PLA films. The release of phenolic compounds and HFM from extract-enriched films was promoted in the 95% ethanol simulant due to the matrix swelling and relaxation. Food products packaged with PLA-E160 exhibited slower oxidative degradation during storage, as indicated by the higher ascorbic acid content and hue color in orange juice and lower peroxide content in sunflower oil. Nevertheless, both in vivo and in vitro studies showed no antimicrobial effectiveness from the films, likely due to the limited release of active compounds to the surrounding medium. Thus, almond peel extract conferred valuable properties to PLA films, effectively reducing oxidative reactions in food products sensitive to these deterioration processes. Full article

Recently, a variety of nephrite containing localized pink mineral aggregates has emerged on the market, which is sometimes referred to as “peach blossom jade” by some merchants. Currently, there is limited research on this type of nephrite containing pink minerals, and its detailed mineral composition characteristics and coloration mechanisms remain unclear. In this study, four samples of nephrite containing pink minerals were systematically investigated using conventional gemological tests, as well as modern analytical techniques such as X-ray powder diffraction (XRD), infrared spectroscopy (IR), laser Raman spectroscopy, ultraviolet–visible (UV-Vis) absorption spectroscopy, electron probe microanalysis (EPMA), and X-ray fluorescence spectroscopy (XRF). These techniques were employed to elucidate the mineral composition, chemical composition, spectroscopic features, and coloration origins of the samples. The results indicate that the primary mineral constituent of the samples is tremolite, with accessory minerals including zoisite, muscovite, orthoclase, andesine, diopside, and prehnite. The major chemical components of the samples are SiO₂, CaO, and MgO, along with minor amounts of Al₂O₃, K₂O, and FeO_T. The overall green hue of the samples is positively correlated with Fe content. The pink mineral present in the samples is predominantly Mn-bearing zoisite, and the pink coloration of zoisite is primarily attributed to the energy level transitions of Mn²⁺ at approximately 540 nm and 440 nm. Full article

Accurate localisation is key for the autonomy of mobile robots. Fiducial localisation utilises relative positions of markers physically deployed across an environment to determine a localisation estimate for a robot. Fiducial markers are strictly designed, with very limited flexibility in appearance. This often results in a “trade-off” between visual customisation, library size, and occlusion resilience. Many fiducial localisation approaches vary in their position estimation over time, leading to instability. The Stable Fiducial Marker System (STag) was designed to address this limitation with the use of a two-stage homography detection. Through its combined square and circle detection phases, it can refine detection stability. In this work, we explore the utility of STag as a basis for a stable mobile robot localisation system. Key marker restrictions are addressed in this work through contributions of three new chromatic STag marker types. The hue/greyscale STag marker set addresses constraints in customisability, the high-capacity STag marker set addresses limitations in library size, and the high-occlusion STag marker set improves resilience to occlusions. These are designed with compatibility with the STag detection system, requiring only preprocessing steps for enhanced detection. They are assessed against the existing STag markers and each shows clear improvements. Further, we explore the viability of various materials for marker fabrication, for use in outdoor and low-light conditions. This includes the exploration of “active” materials which induce effects such as retro-reflectance and photo-luminescence. Detection rates are experimentally assessed across lighting conditions, with “active” markers assessed on the practicality of their effects. To encapsulate this work, we have developed a full end-to-end deployment for fiducial localisation under the STag system. It is shown to function for both on-board and off-board localisation, with deployment in practical robot trials. As a part of this contribution, the associated software for marker set generation/detection, physical marker fabrication, and end-to-end localisation has been released as an open source distribution. Full article