Search Results (1,346)

Foreign fiber (FF, plural: FFs) contamination has been demonstrated to have a substantial impact on the quality and profitability of cotton textiles. Machine vision technology, characterized by its non-contact approach and high efficiency, has emerged as the primary solution for detecting FFs in cotton. This paper commences with a precise definition and classification of FF and a concomitant analysis of the mechanisms of contamination. Subsequently, a systematic review of global research advancements in imaging technologies and the evolution of algorithms is conducted. This paper emphasizes the use of X-ray, ultraviolet fluorescence, line laser, polarized light, infrared imaging, and hyperspectral imaging techniques for FF detection. Through a comparative analysis, it reveals the applicable scope and effectiveness of various imaging schemes. Regarding the evolution of algorithms, this paper expounds on the technical development process from traditional image processing to machine learning (ML) and deep learning (DL). The study meticulously examines the strengths and weaknesses of each algorithmic stage. In conclusion, this paper synthesizes the prevailing technical challenges confronting machine vision detection of FFs in cotton and proffers recommendations for future research directions in this domain, emphasizing multi-technology integration, algorithm optimization, and hardware innovations. Full article

We analyzed six years (2019–2025) of Sentinel-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery to quantify how turbidity and water temperature relate to algal blooms in Utah Lake. We generated satellite-derived estimates of chlorophyll-a (chl-a), turbidity, and surface temperature at 600 randomly distributed sample points. Using generalized least squares models, we found that temperature and turbidity explain only a small fraction of the variance in chl-a (temperature coefficients 0.02–0.03; turbidity coefficients −0.18–0.42), and the strength and sign of correlations vary by location. Despite weak linear correlations, we identified a strong nonlinear pattern: 94% of intense bloom events (chl-a > 87 µg/L) occurred when turbidity was below 120 Nephelometric Turbidity Units (NTU), indicating that blooms more often form under low-turbidity conditions. We also found that the first mild blooms of the season (chl-a > 34 µg/L) typically occurred five days after the largest short-term temperature increase (3–12 °C/day) at a given location, but only when blooms first appeared in April. These results suggest that Utah Lake blooms may be light-limited, with turbidity constraining algal growth that would otherwise occur in response to high nutrient levels, while temperature spikes influence early-season bloom initiation. Our findings have direct implications for monitoring and management strategies that target algal blooms on Utah Lake. Full article

Climate variability necessitates the optimization of sowing dates for vegetable crops to stabilize yields and mitigate abiotic stress risks. This study aimed to evaluate the effect of sowing dates on the productivity of daikon radish (Raphanus sativus L. convar. acanthiformis Sazon.) cultivars Gulliver and Minowase under medium-podzolic, light loamy soil conditions with a pH (pHKCl) of 6.74 during the period 2022–2024. Field experiments were conducted across four sowing dates (ranging from July to early August), accounting for the hydrothermal conditions of the growing season. Effective air temperatures ranged from 428 to 950 °C, with precipitation levels between 36.9 and 252.3 mm. It was established that the sowing date significantly influenced daikon yield (p < 0.001). A significant positive correlation was identified between yield and precipitation (r = 0.76–0.84; p < 0.05), whereas the correlation between yield and the sum of effective temperatures was weak to moderate and predominantly negative (r = −0.62 to −0.10). The highest yields were achieved with sowing in the third ten-day period of July: 54.6 t ha⁻¹ for the Gulliver cultivar and 58.9 t ha⁻¹ for the Minowase cultivar. The Minowase cultivar consistently outperformed Gulliver in terms of yield and exhibited higher ecological plasticity under fluctuating hydrothermal conditions. These findings confirm the feasibility of optimizing sowing dates as an effective adaptive tool for enhancing the stability of daikon production amidst climate change. Full article

Digital Light Processing (DLP), which operates through a layer-by-layer deposition, has proven to be a promising technique for obtaining complex and customized architectures. However, there are still numerous unresolved challenges in ceramics additive manufacturing, among which is delamination due to suboptimal adhesion between the layers, which threatens the structural integrity and properties of samples. According to recent findings, excess surface hydroxyl groups were identified as being responsible for this defect; a suitable calcination pre-treatment of the ceramic powder could be effective in significantly mitigating delamination flaws in mullite DLP printed bodies. Therefore, in addition to optimizing the printable slurry formulation and printing parameters (mainly in terms of curing energy and layer resolution), this work aimed at investigating the influence of the calcination of a commercial mullite powder (added with magnesium nitrate hexahydrate, as a precursor of the sintering aid MgO) as a simple and effective treatment to additively shape ceramic bodies with limited flaws and enhanced density. The surface characteristics evolution of the mullite powder was investigated, specifically comparing samples after magnesium nitrate hexahydrate addition and ball-milling in water (labeled as BM), and after an additional calcination (BMC). In particular, the effect of the superficial -OH groups detected by FTIR analysis in the BM powder, but not in the BMC sample, was studied and correlated to the properties of the respective ceramic slurry in terms of rheological behavior and curing depth. The hydrophilicity of BM powders, due to superficial hydroxyls groups, affects ceramic powder dispersion and wettability by the resin, causing a weak interface. At the same time, it promotes photopolymerization of the light-sensitive resin, thus inducing the as-printed matrix embrittlement. Anyhow, its photopolymerization degree, equal to 67% and 55% for BM and BMC, respectively, was enough to guarantee the printability of both slurries. However, the use of BMC significantly reduced flaw occurrence in the as-printed bodies and the final density of the samples sintered at 1450 °C (without an isothermal step) was increased (approx. 60% and 50% of the theoretical value for BMC and BM, respectively). Thus, the target porosity of the ceramic bodies was guaranteed, and their structural integrity achieved without any increase in sintering temperature but with a simple powder treatment. Full article

Segmentation of tomato fruits under complex lighting conditions remains technically challenging, especially in low illumination or overexposure, where RGB-only methods often suffer from blurred boundaries and missed small or occluded instances, and simple multimodal fusion cannot fully exploit complementary cues. To address these gaps, we propose YOLO-MSRF, a lightweight RGB–NIR multimodal segmentation and refinement framework for robust tomato perception in facility agriculture. Firstly, we propose a dual-branch multimodal backbone, introduce Cross-Modality Difference Complement Fusion (C-MDCF) for difference-based complementary RGB–NIR fusion, and design C2f-DCB to reduce computation while strengthening feature extraction. Furthermore, we develop a cross-scale attention fusion network and introduce the proposed MS-CPAM to jointly model multi-scale channel and position cues, strengthening fine-grained detail representation and spatial context aggregation for small and occluded tomatoes. Finally, we design the Multi-Scale Fusion and Semantic Refinement Network, MSF-SRNet, which combines the Scale-Concatenate Fusion Module (Scale-Concat) fusion with SDI-based cross-layer detail injection to progressively align and refine multi-scale features, improving representation quality and segmentation accuracy. Extensive experiments show that YOLO-MSRF achieves substantial gains under weak and low-light conditions, where RGB-only models are most prone to boundary degradation and missed instances, and it still delivers consistent improvements on the mixed four-light validation set, increasing $mA{P}_{0.5}$ by 2.3 points, $mA{P}_{0.5–0.95}$ by 2.4 points, and mIoU by 3.60 points while maintaining real-time inference at 105.07 FPS. The proposed system further supports counting, size estimation, and maturity analysis of harvestable tomatoes, and can be integrated with depth sensing and yield estimation to enable real-time yield prediction in practical greenhouse operations. Full article

The increasing demand for food is the driving force behind the search for novel, more selective, and less hazardous agrochemicals. Natural products are gaining prominence recently due to the promise of being green agrochemicals, but many natural products are poorly soluble in water, which reduces their applicability. In this work, we successfully formulated a water-insoluble Saussurea lappa root extract into a stable aqueous suspension using xanthan gum. The colloidal suspension was characterized by rheology, dynamic light scattering, and zeta potential. The results demonstrated that the suspension is a stable, sprayable, shear-thinning viscoelastic system (weak gel). A series of S. lappa solutions with xanthan gum were prepared and tested against five plant species, observing the inhibitory effect on the shoots and roots. The results were also compared with the commercial herbicide Dual. The S. lappa extract presented results comparable to or even greater than Dual for Lactuca sativa, Cucumis sativus, Brachiaria decumbens, and Bidens pilosa. However, it showed low inhibitory activity for Sorghum bicolor, highlighting its potential for selective weed control. This work illustrates xanthan gum as an effective vehicle for formulating insoluble natural products and demonstrates that S. lappa extract is a promising candidate for developing novel herbicides. Full article

HiRo-SLAM is a visual-inertial SLAM system developed to achieve high accuracy and enhanced robustness. To address critical limitations of conventional methods, including systematic biases from imperfect camera models, uneven spatial feature distribution, and the impact of outliers, we propose a unified optimization framework that integrates four key innovations. First, Precise Camera Projection Modeling (PCPM) embeds a fully differentiable camera model in nonlinear optimization, ensuring accurate handling of camera intrinsics and distortion to prevent error accumulation. Second, Visibility Pyramid-based Adaptive Non-Maximum Suppression (P-ANMS) quantifies feature point contribution through a multi-scale pyramid, providing uniform visual constraints in weakly textured or repetitive regions. Third, Robust Optimization Using Graduated Non-Convexity (GNC) suppresses outliers through dynamic weighting, preventing convergence to local minima. Finally, the Point-Line Feature Fusion Frontend combines XFeat point features with SOLD2 line features, leveraging multiple geometric primitives to improve perception in challenging environments, such as those with weak textures or repetitive structures. Comprehensive evaluations on the EuRoC MAV, TUM-VI, and OIVIO benchmarks show that HiRo-SLAM outperforms state-of-the-art visual-inertial SLAM methods. On the EuRoC MAV dataset, HiRo-SLAM achieves a 30.0% reduction in absolute trajectory error compared to strong baselines and attains millimeter-level accuracy on specific sequences under controlled conditions. However, while HiRo-SLAM demonstrates state-of-the-art performance in scenarios with moderate texture and minimal motion blur, its effectiveness may be reduced in highly dynamic environments with severe motion blur or extreme lighting conditions. Full article

Underwater optical images often exhibit severe color distortion, weak texture, and uneven illumination due to light absorption and scattering in water. These issues result in unstable feature detection and inaccurate image registration. To address these challenges, this paper proposes an underwater image stitching method that integrates ORB (Oriented FAST and Rotated BRIEF) feature extraction with a fixed-ratio constraint matching strategy. First, lightweight color and contrast enhancement techniques are employed to restore color balance and improve local texture visibility. Then, ORB descriptors are extracted and matched via a KNN (K-Nearest Neighbors) nearest-neighbor search, and Lowe’s ratio test is applied to eliminate false matches caused by weak texture similarity. Finally, the geometric transformation between image frames is estimated by incorporating robust optimization, ensuring stable homography computation. Experimental results on real underwater datasets show that the proposed method significantly improves stitching continuity and structural consistency, achieving 40–120% improvements in SSIM (Structural Similarity Index) and PSNR (peak signal-to-noise ratio) over conventional Harris–ORB + KNN, SIFT (scale-invariant feature transform) + BF (brute force), SIFT + KNN, and AKAZE (accelerated KAZE) + BF methods while maintaining processing times within one second. These results indicate that the proposed method is well-suited for real-time underwater environment perception and panoramic mapping on low-cost, micro-sized underwater robotic platforms. Full article

Artificial cyanobacterial crusts (ACCs) are a potentially effective biological strategy for combating desertification. However, while functional microorganisms influence ACCs formation efficiency, research on their role is limited, and their underlying promotion mechanisms remain unclear. Here, we investigated the effects of three functional synthetic microbial communities (SynComs), each dominated by microorganisms specialized in exopolysaccharide (EPS) production (3 strains), siderophore production (3 strains), or nitrogen fixation (4 strains), on ACCs formation following inoculation with Microcoleus vaginatus. This study was carried out in a controlled laboratory setting with a 12 h light/dark cycle and a light intensity of 2400–2700 lux. Following a 24-day cultivation period, EPS-producing or nitrogen-fixing SynComs significantly increased the chlorophyll-a content by 16.0–16.3%. Except for the nitrogen-fixing bacteria treatment, other SynComs enhanced the soil organic matter content of ACCs by 9.1% to 27.3%. The content of EPS was significantly improved by all three SynComs by 14.1~19.2%. Urease activity rose by 6.7% when siderophore-producing bacteria were added. The impacts of SynComs on ammonium nitrogen (NH₄⁺-N) showed different temporal dynamics: nitrogen-fixing SynComs significantly increased NH₄⁺-N early (≤10 days), while EPS-producing and siderophore-producing SynComs enhanced accumulation later (17–24 days). SynComs inoculation markedly accelerated cyanobacterial and general microbial colonization and growth. In comparison to day 0, the 16S rRNA gene copy number of ACCs increased by 24.1% and 43.0%, respectively, in the EPS-producing and nitrogen-fixing SynComs. Additionally, correlation analysis showed that SynComs transformed the weak correlations in the control into a strong positive correlation between NH₄⁺-N and both Chl-a and microbial biomass. Our findings demonstrate SynComs, particularly the EPS-producing or nitrogen-fixing SynComs, enhance ACCs formation through elucidated mechanisms, providing a theoretical basis for optimizing ACCs-based desertification control strategies. Full article

Surface plasmon resonance (SPR) sensors based on photonic crystal fibers (PCFs) hold significant promise for high-precision detection in biochemical and chemical sensing. However, achieving high sensitivity in low-refractive-index (RI) aqueous environments remains a formidable challenge due to weak light-matter interactions. To address this limitation, this paper designs and proposes a novel dual-channel D-shaped PCF-SPR sensor tailored for the refractive index range of 1.34–1.40. The sensor incorporates a dual-layer gold/titanium dioxide film, with gold nanoparticles deposited on the surface to synergistically enhance both propagating and localized surface plasmon resonance effects. Furthermore, a D-shaped polished structure integrated with double-sided microfluidic channels is employed to significantly strengthen the interaction between the guided-mode electric field and the analyte. Finite element method simulations demonstrate that the proposed sensor achieves an average wavelength sensitivity of 5733 nm/RIU and a peak sensitivity of 15,500 nm/RIU at a refractive index of 1.40. Notably, the introduction of gold nanoparticles contributes to an approximately 1.47-fold sensitivity enhancement over conventional structures. This work validates the efficacy of hybrid plasmonic nanostructures and optimized waveguide design in advancing RI sensing performance. Full article

This study investigated the effects of alkaline pretreatment and drying methods on the physicochemical properties of Gelidium amansii and the quality of the resulting agar jelly. Seaweeds with or without alkaline pretreatment were subjected to either sun-drying or shortwave infrared (SWIR) lamp-drying for three or seven cycles to evaluate whether SWIR drying could replace conventional sun-drying by reducing drying time and whether alkaline pretreatment could enhance gel hardness. The results showed that both drying methods effectively reduced moisture content, while the alkaline pretreatment significantly increased the ash content, likely due to the removal of water-soluble components. Marked color improvement was observed after seven cycles of sun-drying or following alkaline pretreatment, with the appearance changing from purplish red to bright golden yellow, which is closer to traditional quality expectations. Although SWIR lamp-drying was more energy-efficient, it resulted in limited color improvement. Volatile compound analysis revealed that deviations from the fresh control increased with the number of sun-drying cycles, whereas alkaline pretreatment and infrared-drying induced more pronounced changes in volatile profiles. Among all of the treatments, Gelidium subjected to seven sun-drying cycles produced jellies with the most favorable texture, indicating enhanced agar gel formation through repeated washing and drying. In contrast, the combination of alkaline pretreatment and infrared-drying restricted agar extraction, likely due to tissue hardening and insufficient light intensity, resulting in weak or negligible gel formation. Overall, both the drying method and alkaline pretreatment significantly influenced the Gelidium quality and agar gel properties; despite being labor-intensive, traditional washing and sun-drying processes remain critical for achieving desirable product quality. Full article

Accurately assessing collapse risks of high-elevation, concealed rock mass blocks within the steep cliffs of Bijiashan, Three Gorges Reservoir Area, is challenging. This study employed a multidimensional approach—integrating airborne Light Detection and Ranging (LiDAR), the transient electromagnetic method (TEM), close-range photogrammetry, horizontal drilling, and borehole optical imaging—to characterize the rock mass structure of the WD1 cliff belt and delineate 52 individual blocks. Stability analysis incorporated stereographic projection for macro-scale assessment and employed mechanical models specific to three primary failure modes (toppling, sliding, falling). Finite element strength reduction quantified the stress–strain response of a representative block under natural and rainstorm conditions. Particle Flow Code (PFC) simulated dynamic instability of the exceptionally large block W1-37. Results indicate the WD1 rock mass is highly fractured, with base sections prone to weakness. Toppling failure dominates (90.4%). Under rainstorm conditions, the average Factor of Safety (FOS) decreased by 14.7%, and 73.1% of the blocks that were stable under natural conditions were destabilized—specifically transitioning to marginally stable or substable states—often triggering chain-reaction instability characterized by “crack propagation—base buckling”. W1-37 exhibited staged failure under rainstorm: “strain localization at fissure tips—penetration of basal cracks—overturning of the upper rock mass”. Its frontal rock reached a peak sliding velocity of 15.17 m/s, indicative of base-breaking toppling. The integrated “multi-technology survey—multi-method evaluation—multi-scale simulation” framework provides a quantitative basis for risk assessment of rock mass disasters in the Three Gorges Reservoir Area and offers a technical paradigm for similar high-steep canyon regions. Full article

The abrupt COVID-19 lockdown in early 2020 offered a unique natural experiment to examine vegetation productivity responses to sudden declines in human activity. Although vegetation often responds to environmental changes with time lags, how such lags operate under short-term, intensive disturbances remains unclear. This study combined multi-source environmental data with an interpretable machine learning framework (XGBoost-SHAP) to analyze spatiotemporal variations in net primary productivity (NPP) across the Beijing-Tianjin-Hebei region during the strict lockdown (March–May) and recovery (June–August) periods, using 2017–2019 as a baseline. Results indicate that: (1) NPP showed a significant increase during lockdown, with 88.4% of pixels showing positive changes, especially in central urban areas. During recovery, vegetation responses weakened (65.31% positive) and became more spatially heterogeneous. (2) Integrating lagged environmental variables improved model performance (R² increased by an average of 0.071). SHAP analysis identified climatic factors (temperature, precipitation, radiation) as dominant drivers of NPP, while aerosol optical depth (AOD) and nighttime light (NTL) had minimal influence and weak lagged effects. Importantly, under lockdown, vegetation exhibited stronger immediate responses to concurrent temperature, precipitation, and radiation (SHAP contribution increased by approximately 7.05% compared to the baseline), whereas lagged effects seen in baseline conditions were substantially reduced. Compared to the lockdown period, anthropogenic disturbances during the recovery phase showed a direct weakening of their impact (decreasing by 6.01%). However, the air quality improvements resulting from the spring lockdown exhibited a significant cross-seasonal lag effect. (3) Spatially, NPP response times showed an “urban-immediate, mountainous-delayed” pattern, reflecting both the ecological memory of mountain systems and the rapid adjustment capacity of urban vegetation. These findings demonstrate that short-term removal of anthropogenic disturbances shifted vegetation responses toward greater immediacy and sensitivity to environmental conditions. This offers new insights into a “green window period” for ecological management and supports evidence-based, adaptive regional climate and ecosystem policies. Full article

A method for quality inspection of food packaging based on hybrid imaging and machine-learning techniques is presented. The proposed inspection system integrates thermal and visible-light imaging, enabling detection and classification of faults such as weak seals, creases and contamination. For the purpose of the study data acquisition is automated with the use of an industrial manipulator, ensuring repeatability and consistent positioning of samples. Using the acquired images, the temperature distribution in the sealing area and selected process parameters, a predictive model for burst-pressure testing was developed. The proposed workflow includes attribute selection, hyperparameter optimization and the application of regression algorithms. The proof-of-concept results demonstrate a strong alignment between predicted and measured values, as well as high model stability. The best-performing model, ElasticNet, achieved an R² of 0.815 and an MAE of 0.028 kgf/cm², confirming its potential for non-destructive quality control of packaging. Full article

This paper systematically analyzes the propagation, transformation, and accumulation mechanisms of multi-source noise and device non-idealities within the complete signal chain from the transmitter through the channel to the receiver, focusing on wireless optical coherent communication systems from a signal propagation perspective. It establishes the stepwise propagation process of signals and noise from the transmitter through the atmospheric turbulence channel to the coherent receiver, clarifying the coupling mechanisms and accumulation patterns of various noise sources within the propagation chain. From a signal propagation viewpoint, the study focuses on analyzing the impact mechanisms of factors, such as Mach–Zehnder modulator nonlinear distortion, atmospheric turbulence effects, 90° mixer optical splitting ratio imbalance, and dual-balanced detector responsivity mismatch, on system bit error rate performance and constellation diagrams under conditions of coexisting multiple noises. Simultaneously, by introducing differential and common-mode processes, the propagation and suppression characteristics of additive noise at the receiver end within the balanced detection structure were analyzed, revealing the dominant properties of different noise components under varying optical power conditions. Simulation results indicate that within the range of weak turbulence and engineering parameters, the impact of modulator nonlinearity on system bit error rate is relatively minor compared to channel noise. Atmospheric turbulence dominates system performance degradation through the combined effects of amplitude fading and phase perturbation, causing significant constellation spreading. Imbalanced optical splitting ratios and mismatched responsivity at the receiver weaken common-mode noise suppression, leading to variations in effective signal gain and constellation stretching/distortion. Under different signal light power and local oscillator light power conditions, the system noise exhibits distinct dominant characteristics. Full article