Search Results (795)

Greenhouses in subtropical and tropical regions must be designed as agricultural engineering systems adapted to local climates, rather than simply replicating the “insulation” models of temperate areas. Under extreme climatic conditions such as persistent high temperatures, intense solar radiation, high humidity, heavy rainfall, and frequent extreme winds, greenhouses transform from enclosed insulation layers into selective climate filters, mitigating crop stress while maintaining close contact with the outdoor environment. This paper summarizes how these climate drivers are reshaping the use, structure, and control concepts of greenhouses, emphasizing that the performance of warm-zone greenhouses depends primarily on heat dissipation, humidity management, and biohazard control, rather than heating and insulation. In this review, we analyze the climatic boundary conditions that define warm-climate conservation cultivation, including long-term overheating risk, high UV radiation, vapor pressure deficit, and suppressed condensation tendency, as well as storm-induced uplift and dynamic loads. These constraints necessitate unique structural forms: tall, lightweight, well-ventilated building types with large roof and side openings, roof geometries that facilitate rainwater runoff, sophisticated drainage systems, and corrosion-resistant materials suitable for humid and coastal environments. Because insect netting significantly reduces ventilation, pest control and temperature regulation become co-design issues, requiring oversized vents, optimized airflow paths, and hybrid roof–mesh structures. Ventilation is considered the primary climate-control mechanism, supplemented by passive cooling measures such as shading and radiation/optical management (e.g., diffuse films and near-infrared-selective films). Active evaporative cooling is considered a conditional measure due to humidity limitations and disease risks. This paper also integrates the impacts on specific crops (fruits and vegetables, leafy greens, and orchids). It highlights emerging trends: typhoon-resistant and adaptive geometries, computational fluid dynamics (CFD)-based design, and sensor-rich IoT/digital twin control frameworks. These principles collectively establish a coherent design framework for achieving resilient, resource-efficient greenhouse production in warm climates. Full article

Timely wildfire monitoring depends critically on optical and thermal infrared sensor observations from spaceborne instruments. The TS-SatFire benchmark (2025) consolidates multispectral VIIRS image stacks from Suomi-NPP and NOAA-20 for three tasks: active fire (AF) detection, burned area (BA) mapping, and fire progression (FP) prediction. We make two contributions. First, a systematic label-quality audit reveals that many fires lack ground-truth annotations; 18 training fires and 2 test fires were excluded for AF, and the two unannotated test fires cannot be scored by any model. We further document the benchmark’s scoring procedure, which differs from ours in ways that make the two sets of figures incomparable, and the BA label encoding in the released GeoTIFFs; the BA task is only audited. Second, we propose SpaSE-UNet3D, a spatial squeeze-and-excitation 3D U-Net whose spatial-only $(1,3,3)$ convolutions avoid temporal mixing on short observation windows, while SE channel attention reweights the VIIRS spectral bands dynamically. With micro-averaging over all test pixels, it reaches F1 = $0.8549\pm 0.0005$ on AF and $0.3845\pm 0.0221$ on FP at TS = 2, matching or exceeding the strongest published baselines on their respective terms. A single-day AF input reaches $0.8520\pm 0.0008$, within 0.003 of the two-day figure, indicating that one acquisition carries most of the detectable signal, whereas published baselines use up to six days; on FP, we use one third of their temporal context. An ablation shows the spatial-only design matches the accuracy of a full $(3,3,3)$ network with $2.72\times$ fewer parameters. Code and results are publicly available. Full article

Capturing reliable hyperspectral imager data at a meaningful frame rate for explosions and other fast-changing scenes is not possible in the mid-wave infrared region under traditional sensor operating configurations and processing techniques, which typically have frame rates on the order of 0.5–2.0 Hz. To combat these shortcomings, the scene acquisition parameters were tailored for explosions and a new method for processing optical signatures of fast transient scenes with Fourier-transform infrared hyperspectral imagers was developed. For this technique, the instrument was first configured to collect asymmetric interferograms while optimizing the number of measurement points on the short side of the interferogram. Additionally, pixel-wise zero path distance offset and phase corrections were applied to the interferograms, a reduced spectral resolution of 8 cm⁻¹ was selected, and the window size was narrowed to 32 × 64 pixels while using a lens with a wide field of view. The smooth offset correction for scene change artifacts was then applied in post-processing to address any remaining artifacts in the Fourier-transformed spectra. These procedures yielded a 29× increase in frame rate and significant improvements in spectra fidelity. This work makes reliable field calibrations and measurements of explosions with Fourier-transform infrared hyperspectral imagers more achievable than before. Full article

Crop residue cover (CRC) plays a crucial role in sustainable farming systems by improving soil structure, regulating water retention, and reducing soil erosion. Accurate CRC monitoring is therefore important for evaluating field management practices at scale. Existing field methods (e.g., line-transect and visual estimation) are labor-intensive and difficult to scale, while optical retrievals are often confounded by soil moisture. Moisture introduces nonlinear spectral distortions that can bias residue estimates, particularly in the shortwave infrared range. We propose a Deep Moisture-Invariant Autoencoder (DMIA) framework that performs conditional spectral normalization—referred to as moisture normalization (dry-equivalent spectral transformation)—before linear spectral unmixing. The workflow has two stages: (1) a conditional autoencoder that transforms moisture-affected spectra to dry-equivalent spectra, and (2) fully constrained linear unmixing on dry-equivalent spectra. The experiment included 63 controlled wheat-residue scenes at a semi-arid site in Morocco, spanning three moisture levels and seven residue proportions (0–100%) measured with ASD spectroscopy. Within this controlled experimental dataset, DMIA achieved a global coefficient of determination of R² = 0.93, outperforming ordinary least squares (R² = 0.65), fully constrained least squares (R² = 0.68), and ELMM (R² = 0.71), and matching the performance of MESMA (R² = 0.93) while requiring only a single forward pass at inference rather than iterative library matching. Although both methods showed similar overall accuracy, a closer analysis reveals that DMIA’s advantage over MESMA widens under wetter, coarser-resolution conditions, which are highly representative of operational monitoring. This finding is further validated by a Monte Carlo uncertainty propagation, proving the results are unaffected by reference noise. Using spectrally resampled ground data to simulate satellite responses, performance remained robust for PRISMA (R² = 0.93) and Sentinel-2 simulation (R² = 0.87). Reconstruction diagnostics (mean SAM below 5°) support the physical plausibility of the learned transformation. These results suggest that conditional spectral normalization can reduce moisture-related distortions while preserving compositional signals under controlled experimental conditions; however, the use of three discrete moisture levels represents an experimental simplification; in open operational fields, soil moisture varies continuously and pixel-level states are unknown. This framework provides a proof-of-concept basis for further investigation across diverse soils, residue types, and operational sensor configurations. Full article

In this study, zinc-doped magnesium ferrite (Zn_x-Mg_1−xFe₂O₄) nanoparticles were synthesized using a facile combustion method and investigated for their electrochemical sensing and photocatalytic applications. The structural, morphological, and optical properties of the synthesized nanoparticles were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), Energy-dispersive X-ray spectroscopy (EDAX), Fourier-transform infrared spectroscopy (FTIR), Energy band gap (Eg) and UV-Vis spectroscopy. The synthesized Zn–MgFe₂O₄ nanoparticles exhibited crystallite sizes ranging from 18.7 to 27.9 nm with an optical band gap of 1.86–1.89 eV. The catalyst achieved degradation efficiencies of 78% for Eriochrome Black T and 85% for Methyl Orange within 120 min, while the electrochemical sensor exhibited excellent linearity toward HgCl₂ detection (R² = 0.99664), demonstrating the multifunctional capability of the synthesized nanostructure. The synergistic effects of Zn doping contributed to enhanced electrical conductivity, catalytic activity, and structural stability. The novelty of this work lies in the development of combustion-synthesized Zn–MgFe₂O₄ nanoparticles as a multifunctional material capable of simultaneously achieving efficient photocatalytic degradation of organic dyes and sensitive electrochemical detection of mercury chloride using a simple and scalable synthesis route. These findings demonstrate that Zn–MgFe₂O₄ nanoparticles hold significant potential for integrated environmental remediation and electrochemical sensing applications. Full article

Visual geo-localization is a predominant approach for unmanned aerial vehicles (UAVs) operating in Global Navigation Satellite System (GNSS)-denied environments, typically achieved by matching UAV-captured visible optical images with satellite base maps. However, under low-light conditions, visible cameras struggle to capture distinct features. While infrared sensors can capture clear features in such scenarios, the significant modality gap between thermal infrared images and optical satellite base maps makes accurate matching highly challenging. In this paper, we propose a novel cross-modal super-resolution matching and geo-localization method constrained by geographic consistency. First, a geographic consistency normalization module is introduced to narrow the modality gap between satellite optical images and thermal infrared images, thereby enhancing cross-modal matchability. Subsequently, a thermal infrared super-resolution enhancement module is employed to improve the spatial resolution and detail representation of the images, effectively increasing feature discriminability in low-texture regions. Finally, an end-to-end dense matching module is utilized to strengthen the stability of cross-modal correspondence estimation, ultimately improving geo-localization accuracy in low-light environments. Extensive experiments conducted on both a self-constructed network dataset and a real-world flight dataset demonstrate that the proposed method outperforms current competitive approaches. The proposed framework is not a simple combination of existing enhancement and matching modules, but a task-driven design that jointly addresses cross-modal discrepancy, low-resolution thermal imagery, and robust correspondence estimation. Experiments on self-constructed and public datasets demonstrate its robustness and superiority, achieving average geo-localization errors of 1.31 m and 8.04 m, respectively. Full article

Photoelectric intelligent sensor chips have become a key technology for next-generation intelligent sensing by integrating photonic devices, electronic circuits, and artificial intelligence algorithms. Recent advances in silicon photonics, two-dimensional materials, heterogeneous integration, and intelligent signal processing have significantly improved their sensitivity, response speed, and integration capability. This review presents a comprehensive overview of photoelectric intelligent sensor chips from fundamental principles to system-level applications. The operating mechanisms, device architectures, fabrication technologies, and photonic integration strategies are summarized, followed by recent progress in industrial, medical, and intelligent sensing applications. Current technical challenges, including material quality, heterogeneous integration, power consumption, and intelligent data processing, are also discussed. Finally, future trends toward highly integrated, low-power, and AI-enabled sensing systems are highlighted. This review provides a concise reference for the development of next-generation photoelectric intelligent sensor chips and their practical applications. Full article

This study presents a novel approach in millimeter wave (MMW) imaging by demonstrating the first experimental implementation of an imaging system that integrates optical up-conversion within a glow discharge detector (GDD) plasma alongside a phase-sensitive lock-in camera. In this novel approach, incident MMW radiation, modulated with an ON–OFF signal, strikes the GDD, which functions as an up-conversion sensor, transforming the MMW signal into ON–OFF-modulated near-infrared (NIR) optical emissions. This upconverted light is then captured by the lock-in camera, which synchronously demodulates the optical signal at each pixel. By simultaneously receiving the upconverted optical emissions and the original reference signal used to modulate the MMW radiation, the camera achieves synchronous demodulation of the incoming light at each pixel. This advanced configuration enables the direct extraction of amplitude and phase information with pixel-level synchronous demodulation, while actively and effectively increasing the SNR and eliminates the uncorrelated background plasma emissions. The proposed optical up-conversion system effectively addresses existing limitations, offering a unique combination of advantages. By inheriting the fundamental benefits of GDD, such as cost-efficiency per unit, simplified electronic design, and robust resistance to high radiation levels, the system demonstrates enhanced noise suppression and increased sensitivity. Validation experiments utilizing LED modulation verify an optical acquisition time of approximately 100 ms per scan position (pixel dwell time), representing a significant improvement of several orders of magnitude over conventional optical acquisition approaches. In the present proof-of-concept implementation, complete image formation additionally includes the mechanical scanning time required for image reconstruction. Ultimately, this work establishes an improved paradigm for MMW imaging, advancing the development of compact, rapid, and cost-effective imaging systems for advanced non-ionizing imaging applications. Full article

Since its launch in 2015, the Sentinel-2 mission has become a cornerstone of moderate-resolution vegetation monitoring, enabling spatially explicit and temporally dense observations of terrestrial ecosystems. Its combination of 10–20 m spatial resolution, a revisit interval of less than five days, and a spectral configuration including red-edge and Short-Wave Infrared (SWIR) bands has transformed optical vegetation monitoring beyond coarse-resolution greenness products. This review synthesises the use of Sentinel-2 for vegetation monitoring, with emphasis on phenology and growth dynamics, biomass and carbon estimation, vegetation stress detection, and associated methodological developments. A systematic Scopus search identified 1700 publications, of which 1097 studies were retained following thematic and methodological screening. The results reveal rapid growth in Sentinel-2-based research after 2018, reflecting its transition into a widely adopted data source supported by cloud-based processing platforms and harmonised data products. Research output is concentrated in a limited number of journals and regions, with Europe and Asia dominating contributions, while other regions remain underrepresented. Phenology and growth monitoring, biomass and carbon assessment, and vegetation stress analysis emerged as the principal application domains. Across these themes, methodological development has shifted from vegetation indices towards machine learning, hybrid radiative-transfer modelling, and multi-sensor data fusion. The reviewed evidence indicates that no single methodological approach consistently outperforms others; rather, performance depends on the target variable, ecosystem characteristics, and the treatment of observational uncertainty. Sentinel-2 has transformed vegetation monitoring by enabling spatially explicit assessment of vegetation phenology, biomass, carbon dynamics, and stress across ecosystems. However, important challenges remain, including uncertainty propagation, limited sensitivity to early physiological stress, the absence of thermal observations, and uneven validation across ecosystem types. Future progress will depend on uncertainty-aware retrieval frameworks, physically informed hybrid models, multi-sensor integration, and expanded calibration and validation across underrepresented ecosystems. Full article

The objective of this research is the development of a copper-based optical sensor for the detection of ovarian cancer marker CA 125 synthesized using low-temperature plasma. Optical materials produced with metals show unique advantages due to their ability to interact with light. There are different methods currently used for the synthesis of optical materials that can be associated with longer processing times and low material yield. The novelty of this study is the development of copper-based optical material (CuPy) using low-temperature plasma and subsequent modification for the detection of CA 125. Introduction: Plasma consists of a mixture of fully and partially ionized gas. It comprises diverse, highly energized species of atoms, ions, electrons, excited molecules, and charged species. These energized species are used to create new materials, for surface modifications, and in medical applications. Plasma can create a controlled environment for the creation of novel materials. Using low-temperature plasma, it will be possible to have precise control of the chemical composition and structure due to the creation of excited molecules, ions, and free radicals. Method: The CuPy material was synthesized using radio-frequency-assisted low-temperature plasma. Prior to synthesis, the plasma chamber was cleaned using radio frequency (RF) plasma without any reagents or gases. RF plasma was used for the synthesis of CuPy for 10 min and subsequent hydrogen plasma (50 sccm) for another 10 min. Two types of products were extracted from the chamber (one in water and another in methanol). These two products were analyzed using UV–visible absorbance spectroscopy, fluorescence spectroscopy, X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared spectroscopy (FTIR). The methanol extracted samples were further modified with CA 125 antibody. Zeta potential measurements were performed to confirm the binding of the CA 125 antibody to the sensor. The sensing efficacy of the sensor towards CA 125 antigen was monitored using fluorescence spectroscopy. Results: The absorbance spectrum of methanol extracted CuPy shows absorbances around 251 nm, 282 nm, and 339 nm. The extracted product exhibited a red edge excitation emission in the visible region. The elemental composition and oxidation state of the sample were evaluated using XPS. CA 125 antibody conjugation with CuPy was confirmed using UV–visible absorbance spectroscopy, fluorescence spectroscopy, and FTIR spectroscopy. The antibody binding resulted in the fluorescence shifts towards higher wavelengths with an increase in the emission intensity compared with CuPy. Zeta potential measurements also confirmed the binding of the CA 125 antibody to the sensor. Different concentrations of CA 125 antigen resulted in the quenching of fluorescence. This change in the fluorescence intensity was used for the detection of CA 125. Conclusions: A copper-based optical material was developed using low-temperature plasma, and it was found to be effective for the detection of CA 125 ovarian cancer marker. Full article

LiDAR sensors provide reliable physical distance measurements using laser signals, enabling accurate acquisition of 3D information for various optical systems. However, they are costly, require significant weight and space, and their reliability and accuracy degrade under adverse environmental and weather conditions. In contrast, thermal cameras operating in the infrared spectrum can capture stable visual information even in challenging scenarios such as nighttime, low-light, and rain. However, they cannot directly provide the physical 3D depth information that LiDAR offers. To design efficient optical systems, there is a growing need for techniques that transform thermal image data into LiDAR-like depth information. While deep learning models can theoretically learn direct mappings between thermal and LiDAR modalities, the scarcity of acquiring paired thermal–LiDAR datasets and the difficulty of acquiring them make this task challenging. In this paper, we propose a thermal image-to-LiDAR depth transformation framework. Our method leverages large-scale pretrained visual models for depth estimation to generate initial depth predictions from thermal inputs. Since pretrained RGB-based models face a modality gap when applied to thermal data, we introduce a two-stage depth refinement. Stage 1 corrects global scale inconsistencies, and Stage 2 refines local structural details. Experiments on the MS2 dataset demonstrate that the proposed framework consistently improves the initial DepthPro outputs across day, night, and rainy conditions. Both quantitative metrics and qualitative comparisons show that RGB-pretrained depth predictions can provide useful structural cues for thermal depth estimation when their global scale and local structural errors are explicitly refined. Full article

Microplastic monitoring needs methods that operate directly in water with minimal sample handling. Conventional techniques such as infrared and Raman spectroscopy and pyrolysis–GC/MS provide polymer-specific information but require sample preparation and delayed laboratory analysis. We propose an optical sensor concept for real-time, in situ microplastic assessment, based on multispectral pulsed transmission in the visible range using synchronized laser-diode lines and the directly transmitted signal through an active sensor volume. After calibration on particle-free water, each particle event reduces to a water-normalized transmission whose deficit is set by geometrical beam–particle overlap and the wavelength-dependent extinction efficiency. The weak polymer absorption is represented by the Urbach-tail formalism, the refractive-index-related redirection of light by a Fresnel-based, surface- and orientation-averaged probability of direct transmission, and particle size and shape are decoupled through an effective optical length. The coupled nonlinear system is solved for the bounds of the polymer absorption coefficient per candidate geometry. Because each polymer occupies a bounded region in multi-wavelength absorption space fixed by its band gap and structural state, the method can, in principle, separate structural modifications of identical composition, such as low- and high-density polyethylene. This is a sensor concept with a model-based proof of concept, not full environmental validation. Experimental verification on real reference particles is reported separately; the present article establishes the measurement model and inversion scheme that this verification builds on. Full article

Multi-sensor fusion of optical and synthetic aperture radar (SAR) imagery is widely used for land cover classification, yet most studies treat heterogeneous sensors as a uniform feature pool, leaving class-dependent differences in sensor contribution insufficiently quantified. To address this gap, this study integrates optical and SAR imagery within a Random Forest (RF) classifier in Google Earth Engine (GEE) and applies a combined SHapley Additive exPlanations (SHAP)—Remove and Retrain (ROAR)—bootstrap framework to disentangle, for each class, which sensor provides which discriminative information and how faithful those attributions are. The dataset included Sentinel-1/2, Korea Multi-Purpose Satellite (KOMPSAT)-3A/5, and Landsat-8, representing a range of spatial resolutions and spectral characteristics. An RF-based machine learning (ML) model was employed to perform multi-sensor data fusion and classification. To address the inherent opacity of ML models, we employed the SHAP algorithm, an explainable artificial intelligence (XAI) method, to interpret classification decisions. SHAP analysis indicated that visible and near-infrared (NIR) bands, along with vegetation indices, were the dominant contributors to land cover classification in this study area, while SAR data provided complementary structural information for spectrally ambiguous targets, such as roads—a class-dependent role reflected in the stable rank ordering of the Sentinel-1 VV contribution across bootstrap replications, rather than in a formally significant magnitude difference. ROAR results were consistent with the top-ranked SHAP features being those on which the classifier relies, supporting a physically plausible interpretation of ML-based remote sensing classification. These attributions were robust to estimator choice (SHAP-permutation ρ = 0.89) and spatial partitioning (mean ρ = 0.97 across folds). The contribution of this study is methodological rather than algorithmic: it provides an integrated analytical framework that applies SHAP, ROAR, and bootstrap confidence intervals jointly to a specific multi-sensor land cover problem, demonstrating that interpretability and high classification accuracy can be reported together. The results demonstrate that optical and SAR fusion contribute differently across land cover classes rather than uniformly, providing practical, class-specific guidance for sensor selection in operational land cover mapping and improving the interpretability of machine learning-based mapping workflows. Full article

Wildfires are significant ecological and environmental disasters, impacting forests, ecosystems, climate stability and human life. The visible-spectrum imagery-based traditional wildfire monitoring system can fail to perform well in the presence of smoke, haze and low lighting. A number of machine learning and deep learning techniques have been proposed, but most of the studies do not provide an integrated Short-Wave Infrared (SWIR) optical-electronic payload framework along with an intelligent optimization technique. The objective of this research is to design an intelligent SWIR-based optical-electronic payload architecture for accurate detection and remote sensing of wildfire and Earth applications via deep learning and optimization techniques. The proposed framework is based on Sentinel-2 SWIR satellite data layers with wildfire and non-wildfire samples. To enhance the quality of the images and the representation of their spectral domain, the following preprocessing operations are carried out: resizing, image normalization, SWIR band extraction, and data augmentation. The following spectral feature extraction techniques are then used: burn area analysis, vegetation stress analysis, and thermal anomaly detection. The framework also incorporates SWIR optical payload design, electronic subsystem development and SWIR InGaAs sensor modeling. Finally, a Hybrid Convolutional Neural Network (CNN)–Residual Network 50 (ResNet50) model optimized by Grey Wolf Optimization (GWO) is used for wildfire classification and hyperparameter tuning. The proposed framework achieved an accuracy of 91.03%, precision of 91.27%, recall of 91.03%, and F1-score of 91.01%. The wildfire detection capability, classification robustness, and convergence performance were enhanced through the integration of SWIR spectral analysis, hybrid deep learning and GWO. The proposed framework offers an effective and trustworthy solution for intelligent wildfire monitoring and Earth remote sensing applications with enhanced spectral sensing and classification performance. Full article

Indoor fire smoke degrades visible-light cameras and near-infrared Lidar through wavelength-dependent absorption and scattering, threatening robotic navigation safety. Existing path planners either ignore sensor degradation or rely on empirical penalties lacking a physical basis. To address these issues, this paper proposes Quality Cost A* (QC-A*), which maps Fire Dynamics Simulator (FDS) visibility fields to sensor perception quality via the Koschmieder and Beer–Lambert physical laws, embedding a cost function that drives paths away from high-attenuation regions. A multi-sensor fusion layer provides fault tolerance under sensor-specific failure conditions. The method is validated through FDS-based simulations across four smoke scenarios in a $20\mathrm{m}\times 6\mathrm{m}$ corridor with 21 obstacles, using 50 start–goal pairs per scenario. Perception quality derives from Beer–Lambert optical transmittance, while the hazard-zone proportion quantifies path segments with visibility below 5 m. Across the Symmetric and Asymmetric scenarios, QC-A* reduces the low-visibility hazard-zone proportion from 40.7% to 19.6% and improves worst-case perception quality from 0.067 to 0.177, with a 15.3% path length increase, while remaining close to traditional A* in light-smoke conditions. Under constructed sensor failure tests, QC-A* maintains a 96–100% planning success rate versus 48% for Camera-Only and 70% for Lidar-Only. QC-A* shifts sensor degradation modeling from empirical penalty to physical mechanism, achieving a favorable safety–efficiency balance prioritizing perceptual safety, and provides an interpretable, generalizable framework for robotic fire-environment path planning. Full article