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Daytime detection of space targets is challenging due to the strong skylight background and the limited resolution of conventional polarization imaging systems. In this work, we present a semiconductor-based polarization detection method that integrates a CMOS polarization imaging sensor with a Schmidt–Cassegrain telescope. To compensate for the spatial resolution loss inherent in division-of-focal-plane semiconductor polarization detectors, a bicubic interpolation algorithm is applied to reconstruct the degree and angle of polarization images. Furthermore, a spectral filtering strategy is introduced to suppress skylight-induced stray radiation, improving image contrast and reducing the risk of detector saturation. The developed system combines semiconductor optoelectronic detection, optical filtering, and computational reconstruction into a compact experimental platform. Validation experiments on Polaris and low-Earth-orbit space targets under daytime conditions demonstrate that the proposed approach achieves clearer and sharper polarization images compared with traditional intensity-based methods. Objective evaluation metrics, including gradient, contrast, brightness, and spatial frequency, confirm significant improvements in image quality. These results highlight the potential of semiconductor optoelectronic devices for polarization-based imaging and provide an effective framework for enhancing daytime space target detection. Full article

Sol–gel-based optical functional sensor coatings were developed for real-time monitoring of multiphase saponification reactions in microreactors. Various pH-sensitive indicator mixtures, including bromocresol green and bromocresol purple (BCG and BCP) and methyl red–methyl orange, were incorporated into sol–gel coatings and evaluated on test plates across pH range of 2–12. Coatings with BCG and BCP 1:3 demonstrated the most pronounced color change at high pH (11–12), with distinct hue (H) transitions providing a reliable measure of local pH. These optimized coatings were integrated into microreactor channels to track the passage of oil and NaOH slugs under varying flow rates. Hue analysis produced reproducible plateaus corresponding to NaOH-rich (H = 50°) and oil-rich (H = 41°) phases, enabling droplet-level resolution of slug flow and detection of flow-regime transitions. The sensor response was fully reversible, highlighting the robustness and reusability of the coatings. Unlike previous high-resolution fluorescence-based systems, this approach relies on simple visible-light imaging and low-cost data extraction, leaving the reaction chemistry unaltered. The results demonstrate that sol–gel coatings coupled with hue-based analysis provide a practical, noninvasive, and real-time monitoring strategy for multiphase reactions in microreactors, with potential for implementation in industrial or IoT-enabled process control systems. Full article

Artificial intelligence (AI)-assisted drone technology in agriculture has transformed productivity and pest control techniques, resulting in novel solutions to modern farming challenges. Drones utilizing sensors, cameras, and AI algorithms can precisely monitor crop health, soil conditions, and insect infestations. Using AI-assisted drones for precision irrigation and yield predictions further improves resource allocation, promotes sustainability, and reduces operating costs. This review examines recent advancements in AI and unmanned aerial vehicles (UAVs) in precision agriculture. Key trends include AI-driven crop disease detection, UAV-enabled multispectral imaging, precision pest management, smart tractors, variable-rate fertilization, and integration with IoT-based decision support systems. This study synthesizes current research to identify technological progress, implementation challenges, scalability barriers, and opportunities for sustainable agricultural transformation. This review of peer-reviewed studies published between 2013 and 2025 uses major scientific databases and predefined inclusion and exclusion criteria covering crop monitoring, precision input application, integrated pest management (IPM), and livestock (especially cattle) monitoring. We describe the platform and payload trade-offs that govern coverage, endurance, and spray quality; the dominant analytics trends, from classical machine learning to deep learning and embedded/edge inference; and the emerging shift from monitoring-only UAV use toward closed-loop decision-making (detection–prediction–intervention). Across the literature, the strongest opportunities lie in robust field validation, multi-modal data fusion (UAV + ground sensors + farm records), and interoperable standards that enable actionable IPM decisions. Key gaps include limited cross-site generalization, scarce reporting of economic indicators (ROI, payback period, and adoption rate), and regulatory and safety barriers for routine autonomous operations. Finally, we present some case studies to emphasize the feasibility and highlight future research directions of AI-assisted drone technology. Through this review, we aim to demonstrate technological advancements, challenges, and future opportunities in AI-assisted drone applications, ultimately advocating for more sustainable and cost-effective farming practices. Full article

Accurate monitoring of nitrogen (N) status is critical for precision N management and optimizing the yield and quality of Zanthoxylum armatum var. novemfolius (ZA). However, individual sensors often struggle to simultaneously capture the biochemical variations and complex canopy structural changes of ZA. Therefore, field experiments were conducted over two consecutive years, applying four N-application rates (0, 150, 300, and 450 kg N ha⁻¹) to ZA. At each phenological stage, hyperspectral imagery and LiDAR point clouds were collected via three UAV flight altitudes (60 m, 80 m, and 100 m), and canopy nitrogen concentration (CNC) and aboveground nitrogen accumulation (AGNA) were measured. This study developed a framework by synergistically fusing UAV-derived hyperspectral imaging (HSI) and LiDAR data for CNC and AGNA monitoring. Results showed that the response of nitrogen status indicators to fertilization was phenology-specific: CNC showed no significant difference (p > 0.05) among treatments during the vigorous vegetative growth stage (VGS) but differed significantly (p < 0.05) during the fruit expansion stage (FES); AGNA differed significantly among treatments at VGS and FES (p < 0.05). The two-step screening yielded NDSI _{(732, 879)} and NDSI _{(560, 690)} as the optimal CNC indicators at VGS and FES, respectively (r = 0.83 and 0.93), whereas the NDSI _{(711, 986)} and NDSI _{(515, 736)} were identified as the optimal AGNA indicators at VGS and FES, respectively (r = 0.91 and 0.71). Across all phenological stages, Random Forest Regression consistently delivered the highest accuracy for CNC (R² = 0.93–0.98, RMSE = 0.87–1.02 g kg⁻¹) and AGNA (R² = 0.95–0.97, RMSE = 1.92–2.55 g plant⁻¹), outperforming MLR, PLSR, and SVR. This synergistic framework provides a high-precision, non-destructive methodology for the precision N monitoring of woody crops. Full article

This study presents a simulation-based evaluation of a wall-following and mapping framework for autonomous underwater vehicles (AUVs) equipped with a single-line laser, targeting structured environments such as rectangular tanks and dam interiors. A hardware-in-the-loop (HIL) simulation platform is developed to integrate sensor emulation, vehicle dynamics, and image-based control while preserving the onboard data formats, update rates, and communication protocols of the AUV system. Using a single camera–laser pair, the framework estimates yaw angle and lateral wall distance from laser image geometry to support real-time wall-following and frontal obstacle avoidance. Wall mapping is performed by transforming laser image features into spatial coordinates and estimating the dimensions of geometric protrusions. The framework is evaluated on simulated walls with protruding features under two navigation conditions: ideal-motion and dynamic-control operation. Simulation results show stable wall-following performance, with lateral distance errors typically below 0.1 m. Under ideal-motion conditions, mapping errors range from 1% to 13%, while under dynamic-control navigation they increase to 10–35% due to attitude fluctuations and control-induced motion. Frontal obstacle avoidance maintains a minimum clearance of 1.04 m. The results demonstrate the feasibility of using a single-line laser and a unified image stream for both real-time wall-following control and post-mission geometric mapping within the defined simulation conditions. While the evaluation is limited to simulation and assumes idealized optical conditions without modeling hydrodynamic disturbances or optical degradation effects, the framework provides a system-level reference for laser-guided inspection strategies in confined underwater environments such as tanks, reservoirs, and dams. Full article

The Middle-Wave Infrared Imaging Spectrometer for Target Asteroids (MIST-A) will be launched in 2028 aboard the Emirates Mission to the Asteroid belt (EMA) and will operate in the 2–5 μm spectral range to study the asteroids’ surface composition and thermo-physical properties. MIST-A’s Optical Head (OH) design is inherited from the Jovian IR Auroral Mapper (JIRAM), from which the instrument also received two spare Hybrid-Thinned Mercury-Cadmium-Telluride (MCT) photodetectors: the Engineering Model EM2 and the Flight Spare FS1. These are tested to assess their performance after a long period of storage. The laboratory setup for testing both detectors consists of a blackbody and a cryostat which houses the focal plane, maintained at temperatures of 85 K, its nominal operative temperature, and 90 K. Two sets of measurements are performed: (1) characterization of the dark current at different integration times (0 ms, 224 ms, 448 ms, 672 ms, 869 ms, 1120 ms); (2) verification of the detectors’ response linearity, measuring a blackbody at different temperatures (from 50 °C to 100 °C), including ambient temperature (25 °C, with the blackbody turned off). The results of these tests confirm that both models are fully operational and allow us to evaluate the consequences of the years of inactivity on their performance. Through a detailed analysis of the detectors’ properties and a comparison study with the results of the sensors’ first characterization performed by their producer in 2009, we come to the conclusion that both instruments are able to fulfill MIST-A’s scientific requirements. The FS1 displays a better performance with respect to the EM2 and for this has been selected as MIST-A’s Flight Model. Full article

Unmanned aerial vehicles (UAVs) provide a flexible platform for surface reflectance measurement at spatial scales between ground observations and satellite remote sensing. This study develops a UAV-based spectroradiometric system for surface reflectance retrieval under natural illumination conditions using non-imaging hyperspectral sensors. The system integrates two stabilized spectroradiometers mounted on a UAV to simultaneously measure hemispherical downwelling irradiance and upwelling surface radiance at flight altitude, enabling reflectance retrieval through a radiance–irradiance ratio framework without relying on ground calibration targets or radiative transfer model inversion. Field experiments were conducted over agricultural plots, and the UAV-derived reflectance was quantitatively validated against ground-based dual-spectroradiometer measurements. The results demonstrate stable irradiance measurements during flight and good agreement between UAV- and ground-derived reflectance across the 400–900 nm spectral range. The proposed system offers a practical and reliable solution for hyperspectral reflectance retrieval using UAV platforms. Full article

X-ray imaging of dense breasts and breast implants often suffers from reduced lesion visibility because strong attenuation lowers contrast, while conventional rhodium (Rh) K-edge filtering suppresses part of the high-energy spectral tail. This study presents a Monte Carlo framework for spectroscopic mammography using a voxelated 1 mm thick cadmium telluride (CdTe) sensor and a first-order detector interaction model to evaluate energy-dependent image quality. The model reproduces fluorescence and inter-voxel energy redistribution in CdTe, but not the full detector chain, and remains idealized with respect to charge transport, carrier collection, threshold dispersion, and pile-up. Energy-resolved simulations in the 10–50 keV range were used to compute spectroscopic contrast-to-noise ratio (CNR) curves and to form integrated-spectrum (IS) images for four tested spectra. For the dense-breast calcium hydroxyapatite (HA) speck detection task considered here, and under the present simulation assumptions, replacing the standard 28 kVp + 50 μm Rh spectrum with 28 kVp + 1 mm Al increased the simulated IS image CNR by 23.11%, with an approximately 5% increase in estimated primary-incident air kerma at the phantom entrance plane. Preliminary experimental implant-phantom images were included as a qualitative feasibility check, showing a trend consistent with simulations. Within the limits of this task-specific simulation, the results suggest that preserving the transmitted high-energy tail can improve HA speck visibility for the present 1 mm CdTe photon-counting detector, with the 28 kVp + 1 mm Al spectrum outperforming the other tested cases. Full article

Traditional vision-based vibration measurement is fundamentally constrained by the low sampling rates of area-scan cameras and the noise sensitivity of existing motion magnification algorithms. To overcome these spatiotemporal barriers, we propose a high-fidelity framework that integrates ultra-high-speed line-scan imaging with a 1D Complex Morlet Wavelet Phase-Based Video Magnification (CMW-PVM) algorithm. By extracting and manipulating the localized phase of 1D spatial signals, CMW-PVM effectively decouples structural dynamics from background noise while eliminating the computational redundancy associated with 2D spatial pyramid methods. Simulations demonstrate that CMW-PVM significantly extends the linear magnification range (up to $\alpha \approx 35$) while preserving exceptional structural fidelity (FSIM $>0.87$) under severe noise conditions (SNR = 10 dB). Experimental validation against a laser Doppler vibrometer (LDV) reveals near-perfect kinematic accuracy, with a relative amplitude error of only 1.65%. Furthermore, at a 100 Hz high-frequency excitation, the system successfully resolves microscopic displacements (≈10 μm) without temporal aliasing—enabled not by violating sampling theory but by leveraging the high physical line rate of the line-scan sensor. This establishes a robust, non-contact, and computationally efficient paradigm for broadband, micro-amplitude vibration monitoring in industrial environments. Full article

Dragon fruit (Hylocereus spp.) is an emerging crop in the tropics and subtropics, but its production is increasingly threatened by diseases that reduce yield and profitability. Early diagnosis of these diseases is crucial for timely intervention, yet visual symptoms often appear only after significant infection has occurred. The study aims to evaluate how optical spectral reflectance can detect dragon fruit diseases and identify the most responsive spectral regions. In this study, six major dragon fruit stem diseases: Neoscytalidium stem canker, stem sunburn, anthracnose, Botryosphaeria stem canker, Bipolaris stem rot, and bacterial soft rot were characterized by the goal of identifying unique spectral signatures for early detection and differentiation of each disease. Seventy-two potted dragon fruit plants of three distinct species were grown under four organic vermicompost treatments (0, 5, 10, 20 tons/acre) in both open-field and high-tunnel conditions together, in a randomized complete block design. A handheld spectroradiometer (350–2500 nm) was used to collect reflectance from the diseased and healthy cladodes (stem segment). Various spectral vegetative indices were computed to identify disease-specific features. The results revealed distinct spectral features for each disease. Infected cladodes consistently exhibited higher reflectance especially in the visible region (400–700 nm) and the near-infrared region (900–2500 nm) of the spectrum than healthy cladodes. The Normalized Difference Vegetative Index (NDVI), Green Normalized Difference Vegetative Index (GNDVI), and Spectral Ratio (SR) spectral indices were significantly higher in healthy plants than in diseased ones, reflecting higher chlorophyll concentration and plant biomass. Conversely, the 1110/810 ratio was lower in healthy plants than in diseased plants, suggesting a more compact internal plant structure. Statistical analysis revealed highly significant differences (p < 0.00001) between healthy and diseased spectra in the Red, Green and NIR regions. Linear Discriminant Analysis(LDA) achieved the highest classification accuracy (OA = 0.642, κ = 0.488), though performance was limited for minority classes. These findings demonstrate that targeted spectral sensing can identify dragon fruit diseases before obvious symptoms emerge. By pinpointing disease-specific spectral indices, our study paves the way for early-warning tools such as targeted multispectral sensors or drone-based imaging that would enable growers to intervene sooner and limit losses. These results highlight the potential for development of UAV-based or portable spectral sensors for large-scale, near real-time disease monitoring in dragon fruit production. Full article

Bio-inspired quadrupedal robots exhibit superior adaptability and mobility in unstructured environments, making them suitable for complex task scenarios such as navigation, obstacle avoidance, and tracking in a variety of environments. Visual perception plays a critical role in enabling autonomous behavior, offering a cost-effective alternative to multi-sensor systems. This paper proposes an image segmentation-guided visual tracking framework to enhance both perception and motion control in quadruped robots. On the perception side, a cascaded convolutional neural network is introduced, integrating a global information guidance module to fuse low-level textures and high-level semantic features. This architecture effectively addresses limitations in single-scale feature extraction and improves segmentation accuracy under visually degraded conditions. On the control side, segmentation outputs are embedded into a biologically inspired central pattern generator (CPG), enabling coordinated generation of limb and spinal trajectories. This integration facilitates a closed-loop visual-motor system that adapts dynamically to environmental changes. Experimental evaluations on benchmark image segmentation datasets and robotic locomotion tasks demonstrate that the proposed framework achieves enhanced segmentation precision and motion flexibility, outperforming existing methods. The results highlight the effectiveness of vision-guided control strategies and their potential for deployment in real-time robotic navigation. Full article

The use of one-step products and their applications in sensory applications has gained much importance. Herein, Schiff’s base fluorescent turn-on sensor, namely FBTS, was synthesised via a condensation reaction between 6-fluorobenzo[d]thiazol-2-amine and 2-hydroxybenzaldehyde. The probe FBTS exhibits an intense “turn-on” blue fluorescence upon binding to Al³⁺ ions in a dimethyl sulfoxide–water (DMSO–H₂O (8:2, v/v)) medium. From photoluminescence (PL) titrations, the detection limit (LOD) for Al³⁺ is estimated to be 0.14 microM, and the Benesi–Hildebrand plot-based association constant (K_a) of 5.4 × 10⁴ M⁻¹ confirm a strong association between FBTS and Al³⁺. Negligible interference is observed in the presence of other metal ions. From the pH effect studies, the optimal pH range for Al³⁺ detection is 7–9. The recyclable reversibility of FBTS + Al³⁺ complex has been demonstrated via the sodium salt of ethylenediaminetetraacetic acid (Na₂-EDTA) chelation. A Job’s plot and interrogations, such as high-resolution mass spectrometry (HR-MS), ¹H-nuclear magnetic resonance (NMR) titration, and density functional theory (DFT), verified the 1:1 stoichiometry of binding between FBTS and Al³⁺. Based on multiple analyses, the binding mode and mechanism have been detailed. In addition, the practical application of FBTS for detecting Al³⁺ is demonstrated using the strip paper method, fish analysis, spiked real sample analysis, and cellular imaging. Full article

This paper presents Diff-GTISR, a novel diffusion-based model for achieving super-resolution in thermal images guided by a high-resolution visible image. Thermal sensors are widely used in surveillance, safety, and industrial inspection; however, their limited spatial resolution constrains thermal image quality because of the low resolution. Thermal image super-resolution is thus critical to compensate for this limitation. The increasing prevalence of multisensor platforms has resulted in the availability of high-resolution visible images, providing effective guidance to enhance thermal image resolution. Recently, diffusion-based super-resolution has demonstrated strong capability in recovering perceptually plausible details; however, such models often underperform in distortion-oriented metrics compared with transformer-based approaches. To address this gap, the proposed Diff-GTISR method employs a modality-specific dual encoder to extract multiscale features and a cross-modal guidance attention module to transfer structural information from visible images into low-resolution thermal images. Also, a refinement network is employed to improve the method further. The experimental results indicate that Diff-GTISR consistently enhances perceptual quality in comparison to state-of-the-art diffusion-based methods. Furthermore, it is superior to transformer-based methods in terms of distortion performance. Full article

The spatial distribution of oyster reefs is an important indicator for assessing environmental changes in nearshore fishery habitats. However, due to tidal fluctuations, images of oyster reef distribution acquired under low-light conditions such as early morning or evening often exhibit common issues such as bright spots and shadows. Thermal infrared (TIR) images, which are unaffected by external lighting conditions, can effectively address this problem. Aerial imaging of Liya Mountain, Haimen, Jiangsu Province, China, was conducted in this study. Based on unmanned aerial vehicles (UAVs) imagery acquired in 2025 using multispectral and TIR sensors, the total oyster reef area was estimated to be 6.61 ha. When compared with the oyster reef distribution derived from visible light aerial imagery collected in 2023 under favorable environmental conditions, this represents a decrease of 0.36 ha (5.4%), with the largest individual reef measuring 3388.17 m². To demonstrate the improvement in extraction accuracy achieved by integrating TIR data with multispectral imagery, the research team compared the extraction accuracy for oyster reefs of different sizes: a 1.91% improvement was observed for small reefs, a 9.02% improvement for middle reefs, and an 18.98% improvement for large reefs. Experimentally, the emissivity of oyster reefs was determined to be 0.982 ± 0.002 using an isothermal method in the laboratory. The emissivity derived from in situ measurements showed similar values, supporting the reliability of the laboratory result and providing a crucial parameter for the inversion of reef surface temperature. Experimental results demonstrate that the TIR band can effectively enhance the spatial accuracy of oyster reef measurements under low-light conditions. Full article

Single-element bolometers have been widely used for medical thermometry to predict a core body temperature based on the measured surface temperature and a pre-determined clinical correlation. The size-of-source effect (SSE) in bolometers is recognized as an important source of error and has been extensively studied such that SSE can be controlled sufficiently to obtain the required accuracy. Thermal imaging cameras relying on much of the same principles have also been widely used for medical thermometry, but recent work has shown that SSE in thermal imaging differs from SSE in single-element bolometers. An unappreciated aspect of this artifact in thermal imaging has been its outsize impact on accuracy, producing more than a degree of inaccuracy in typical scenarios. However, because the artifact has so far avoided a satisfactory characterization, this impact has not been incorporated into the standards and expert recommendations for body thermometry with thermal imaging. In this work, we characterize SSE-like artifact as proportional to the difference between source and non-source temperatures as a function of source size, showing that the same percent deviation artifact is obtained at different source temperatures. We then derive an objective method to obtain convolution kernel parameters and apply these to several thermal imaging sensors and optics configurations. Finally, we show that these methods are sufficient to achieve the required accuracy for body thermometry with thermal imaging. Full article