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Search Results (14,487)

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12 pages, 1973 KB  
Article
A Simple Second-Derivative Image-Sharpening Algorithm for Enhancing the Electrochemical Detection of Chlorophenol Isomers
by Shuo Duan, Yong Wen, Fangquan Xia and Changli Zhou
Chemosensors 2025, 13(10), 372; https://doi.org/10.3390/chemosensors13100372 - 16 Oct 2025
Abstract
Electrochemical detection is widely used in environmental, health, and food analysis due to its portability, low cost, and high sensitivity. However, when analytes with similar redox potentials coexist, overlapping voltammetric signals often occur, which compromises detection accuracy and sensitivity. In this study, a [...] Read more.
Electrochemical detection is widely used in environmental, health, and food analysis due to its portability, low cost, and high sensitivity. However, when analytes with similar redox potentials coexist, overlapping voltammetric signals often occur, which compromises detection accuracy and sensitivity. In this study, a simple second-derivative image sharpening (IS) algorithm is applied to the electrochemical detection of chlorophenol (CP) isomers with similar redox behaviors. Specifically, a graphene-modified electrode was employed for the electrochemical detection of two chlorophenol isomers: ortho-CP (o-CP) and meta-chlorophenol (m-CP) in the range from 1.0 to 10.0 μmol/L. After image-sharpening, the peak potential difference between o- and m-CP increased from 0.08 V to 0.12 V. The limits of detection (LOD) for o-CP and m-CP decreased from 0.6 to 0.9 μmol/L to 0.12 and 0.31 μmol/L, respectively. The corresponding sensitivities also improved from 0.92 to 1.35 A/(mol L−1) to 4.11 and 3.71 A/(mol L−1), respectively. Moreover, the sharpened voltammograms showed enhanced peak resolution, facilitating visual discrimination of the two isomers. These results demonstrate that image sharpening can significantly improve peak shape, peak separation, sensitivity, and detection limit in electrochemical analysis. The obtained algorithm is computationally efficient (<30 lines of C++ (Version 6.0)/OpenCV, executable in <1 ms on an ARM-M0 microcontroller) and easily adaptable to various programming environments, offering a promising approach for data processing in portable electrochemical sensing systems. Full article
(This article belongs to the Section Electrochemical Devices and Sensors)
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73 pages, 2702 KB  
Review
Towards an End-to-End Digital Framework for Precision Crop Disease Diagnosis and Management Based on Emerging Sensing and Computing Technologies: State over Past Decade and Prospects
by Chijioke Leonard Nkwocha and Abhilash Kumar Chandel
Computers 2025, 14(10), 443; https://doi.org/10.3390/computers14100443 (registering DOI) - 16 Oct 2025
Abstract
Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing [...] Read more.
Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production. Full article
20 pages, 6942 KB  
Article
Coherent Dynamic Clutter Suppression in Structural Health Monitoring via the Image Plane Technique
by Mattia Giovanni Polisano, Marco Manzoni, Stefano Tebaldini, Damiano Badini and Sergi Duque
Remote Sens. 2025, 17(20), 3459; https://doi.org/10.3390/rs17203459 - 16 Oct 2025
Abstract
In this work, a radar imagery-based signal processing technique to eliminate dynamic clutter interference in Structural Health Monitoring (SHM) is proposed. This can be considered an application of a joint communication and sensing telecommunication infrastructure, leveraging a base-station as ground-based radar. The dynamic [...] Read more.
In this work, a radar imagery-based signal processing technique to eliminate dynamic clutter interference in Structural Health Monitoring (SHM) is proposed. This can be considered an application of a joint communication and sensing telecommunication infrastructure, leveraging a base-station as ground-based radar. The dynamic clutter is considered to be a fast moving road user, such as car, truck, or moped. The proposed technique is suitable in case of a dynamic clutter, such that its Doppler contribute alias and falls over the 0 Hz component. In those cases, a standard low-pass filter is not a viable option. Indeed, an excessively shallow low-pass filter preserves the dynamic clutter contribution, while an excessively narrow low-pass filter deletes the displacement information and also preserves the dynamic clutter. The proposed approach leverages the Time Domain Backprojection (TDBP), a well-known technique to produce radar imagery, to transfer the dynamic clutter from the data domain to an image plane, where the dynamic clutter is maximally compressed. Consequently, the dynamic clutter can be more effectively suppressed than in the range-Doppler domain. The dynamic clutter cancellation is performed by coherent subtraction. Throughout this work, a numerical simulation is conducted. The simulation results show consistency with the ground truth. A further validation is performed using real-world data acquired in the C-band by Huawei Technologies. Corner reflectors are placed on an infrastructure, in particular a bridge, to perform the measurements. Here, two case studies are proposed: a bus and a truck. The validation shows consistency with the ground truth, providing a degree of improvement within respect to the corrupted displacement on the mean error and its variance. As a by-product of the algorithm, there is the capability to produce high-resolution imagery of moving targets. Full article
20 pages, 1743 KB  
Article
Spatio-Temporal Residual Attention Network for Satellite-Based Infrared Small Target Detection
by Yan Chang, Decao Ma, Qisong Yang, Shaopeng Li and Daqiao Zhang
Remote Sens. 2025, 17(20), 3457; https://doi.org/10.3390/rs17203457 - 16 Oct 2025
Abstract
With the development of infrared remote sensing technology and the deployment of satellite constellations, infrared video from orbital platforms is playing an increasingly important role in airborne target surveillance. However, due to the limitations of remote sensing imaging, the aerial targets in such [...] Read more.
With the development of infrared remote sensing technology and the deployment of satellite constellations, infrared video from orbital platforms is playing an increasingly important role in airborne target surveillance. However, due to the limitations of remote sensing imaging, the aerial targets in such videos are often small in scale, low in contrast, and slow in movement, making them difficult to detect in complex backgrounds. In this paper, we propose a novel detection network that integrates inter-frame residual guidance with spatio-temporal feature enhancement to address the challenge of small object detection in infrared satellite video. This method first extracts residual features to highlight motion-sensitive regions, then uses a dual-branch structure to encode spatial semantics and temporal evolution, and then fuses them deeply through a multi-scale feature enhancement module. Extensive experiments show that this method outperforms mainstream methods in terms on various infrared small target video datasets, and has good robustness under low-signal-to-noise-ratio conditions. Full article
(This article belongs to the Section AI Remote Sensing)
38 pages, 42119 KB  
Article
Automated Mapping of Post-Storm Roof Damage Using Deep Learning and Aerial Imagery: A Case Study in the Caribbean
by Maja Kucharczyk, Paul R. Nesbit and Chris H. Hugenholtz
Remote Sens. 2025, 17(20), 3456; https://doi.org/10.3390/rs17203456 - 16 Oct 2025
Abstract
Roof damage caused by hurricanes and other storms needs to be rapidly identified and repaired to help communities recover from catastrophic events and support the well-being of residents. Traditional, ground-based inspections are time-consuming but have recently been expedited via manual interpretation of remote [...] Read more.
Roof damage caused by hurricanes and other storms needs to be rapidly identified and repaired to help communities recover from catastrophic events and support the well-being of residents. Traditional, ground-based inspections are time-consuming but have recently been expedited via manual interpretation of remote sensing imagery. To potentially accelerate the process, automated methods involving artificial intelligence (i.e., deep learning) can be applied. Here, we present an end-to-end workflow for training and evaluating deep learning image segmentation models that detect and delineate two classes of post-storm roof damage: roof decking and roof holes. Mask2Former models were trained using 2500 roof decking and 2500 roof hole samples from drone RGB orthomosaics (0.02–0.08 m ground sample distance [GSD]) captured in Sint Maarten and Dominica following Hurricanes Irma and Maria in 2017. The trained models were evaluated using 1440 reference samples from 10 test images, including eight drone orthomosaics (0.03–0.08 m GSD) acquired outside of the training areas in Sint Maarten and Dominica, one drone orthomosaic (0.05 m GSD) from the Bahamas, and one orthomosaic (0.15 m GSD) captured in the US Virgin Islands with a crewed aircraft and different sensor. Accuracies increased with a single-class modeling approach (instead of training one dual-class model) and expansion of the training datasets with 500 roof decking and 500 roof hole samples from external areas in the Bahamas and US Virgin Islands. The best-performing models reached overall F1 scores of 0.88 (roof decking) and 0.80 (roof hole). In this study, we provide: our end-to-end deep learning workflow; a detailed accuracy assessment organized by modeling approach, damage class, and test location; discussion of implications, limitations, and future research; and access to all data, tools, and trained models. Full article
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21 pages, 1217 KB  
Article
Optuna-Optimized Pythagorean Fuzzy Deep Neural Network: A Novel Framework for Uncertainty-Aware Image Classification
by Asli Kaya Karakutuk, Ozer Ozdemir and Sevil Senturk
Appl. Sci. 2025, 15(20), 11097; https://doi.org/10.3390/app152011097 - 16 Oct 2025
Abstract
By using Geographic Information Systems, satellite imagery from remote sensing techniques provides quantitative and qualitative data about Earth’s natural and human elements. However, the direct use of raw imagery may prevent the accurate identification of the spectral and temporal characteristics of the target [...] Read more.
By using Geographic Information Systems, satellite imagery from remote sensing techniques provides quantitative and qualitative data about Earth’s natural and human elements. However, the direct use of raw imagery may prevent the accurate identification of the spectral and temporal characteristics of the target objects. To obtain meaningful results from these data, the object and surface features in the image must be classified correctly. In this context, this study develops a new deep learning approach that includes hyperparameter optimization that considers uncertainty factors when classifying satellite imagery. In the proposed approach, a hybrid architecture called CNN-Pythagorean Fuzzy Deep Neural Network (PFDNN) is developed by combining the ability of convolutional neural networks (CNN) to reveal expressive features with the ability of Pythagorean fuzzy set (PFS) theory to predict uncertainty. In addition, to further improve the model’s success, the hyperparameters are automatically optimized using Optuna. In the experiments conducted on the EuroSAT RGB dataset, the CNN+PFDNN+Optuna model achieved 0.9696 ± 0.0037 accuracy and a macro-AUC value of 0.9983, outperforming other methods such as DNN, FDNN, PFDNN and VGG16+PFDNN. Including the Pythagorean fuzzy layer in the system provided about 13.05% higher accuracy than conventional fuzzy systems. These results show that integrating the Pythagorean fuzzy set approach into deep learning models contributes to more effective management of uncertainties in remote sensing data and that hyperparameter optimization significantly impacts model performance. Full article
18 pages, 112460 KB  
Article
Gradient Boosting for the Spectral Super-Resolution of Ocean Color Sensor Data
by Brittney Slocum, Jason Jolliff, Sherwin Ladner, Adam Lawson, Mark David Lewis and Sean McCarthy
Sensors 2025, 25(20), 6389; https://doi.org/10.3390/s25206389 (registering DOI) - 16 Oct 2025
Abstract
We present a gradient boosting framework for reconstructing hyperspectral signatures in the visible spectrum (400–700 nm) of satellite-based ocean scenes from limited multispectral inputs. Hyperspectral data is composed of many, typically greater than 100, narrow wavelength bands across the electromagnetic spectrum. While hyperspectral [...] Read more.
We present a gradient boosting framework for reconstructing hyperspectral signatures in the visible spectrum (400–700 nm) of satellite-based ocean scenes from limited multispectral inputs. Hyperspectral data is composed of many, typically greater than 100, narrow wavelength bands across the electromagnetic spectrum. While hyperspectral data can offer reflectance values at every nanometer, multispectral sensors typically provide only 3 to 11 discrete bands, undersampling the visible color space. Our approach is applied to remote sensing reflectance (Rrs) measurements from a set of ocean color sensors, including Suomi-National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS), the Ocean and Land Colour Instrument (OLCI), Hyperspectral Imager for the Coastal Ocean (HICO), and NASA’s Plankton, Aerosol, Cloud, Ocean Ecosystem Ocean Color Instrument (PACE OCI), as well as in situ Rrs data from National Oceanic and Atmospheric Administration (NOAA) calibration and validation cruises. By leveraging these datasets, we demonstrate the feasibility of transforming low-spectral-resolution imagery into high-fidelity hyperspectral products. This capability is particularly valuable given the increasing availability of low-cost platforms equipped with RGB or multispectral imaging systems. Our results underscore the potential of hyperspectral enhancement for advancing ocean color monitoring and enabling broader access to high-resolution spectral data for scientific and environmental applications. Full article
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21 pages, 2309 KB  
Review
Joint Acidosis and Acid-Sensing Receptors and Ion Channels in Osteoarthritis Pathobiology and Therapy
by William N. Martin, Colette Hyde, Adam Yung, Ryan Taffe, Bhakti Patel, Ajay Premkumar, Pallavi Bhattaram, Hicham Drissi and Nazir M. Khan
Cells 2025, 14(20), 1605; https://doi.org/10.3390/cells14201605 - 16 Oct 2025
Abstract
Osteoarthritis (OA) lacks disease-modifying therapies, in part because key features of the joint microenvironment remain underappreciated. One such feature is localized acidosis, characterized by sustained reductions in extracellular pH within the cartilage, meniscus, and the osteochondral interface despite near-neutral bulk synovial fluid. We [...] Read more.
Osteoarthritis (OA) lacks disease-modifying therapies, in part because key features of the joint microenvironment remain underappreciated. One such feature is localized acidosis, characterized by sustained reductions in extracellular pH within the cartilage, meniscus, and the osteochondral interface despite near-neutral bulk synovial fluid. We synthesize current evidence on the origins, sensing, and consequences of joint acidosis in OA. Metabolic drivers include hypoxia-biased glycolysis in avascular cartilage, cytokine-driven reprogramming in the synovium, and limits in proton/lactate extrusion (e.g., monocarboxylate transporters (MCTs)), with additional contributions from fixed-charge matrix chemistry and osteoclast-mediated acidification at the osteochondral junction. Acidic niches shift proteolysis toward cathepsins, suppress anabolic control, and trigger chondrocyte stress responses (calcium overload, autophagy, senescence, apoptosis). In the nociceptive axis, protons engage ASIC3 and sensitize TRPV1, linking acidity to pain. Joint cells detect pH through two complementary sensor classes: proton-sensing GPCRs (GPR4, GPR65/TDAG8, GPR68/OGR1, GPR132/G2A), which couple to Gs, Gq/11, and G12/13 pathways converging on MAPK, NF-κB, CREB, and RhoA/ROCK; and proton-gated ion channels (ASIC1a/3, TRPV1), which convert acidity into electrical and Ca2+ signals. Therapeutic implications include inhibition of acid-enabled proteases (e.g., cathepsin K), pharmacologic modulation of pH-sensing receptors (with emerging interest in GPR68 and GPR4), ASIC/TRPV1-targeted analgesia, metabolic control of lactate generation, and pH-responsive intra-articular delivery systems. We outline research priorities for pH-aware clinical phenotyping and imaging, cell-type-resolved signaling maps, and targeted interventions in ‘acidotic OA’ endotypes. Framing acidosis as an actionable component of OA pathogenesis provides a coherent basis for mechanism-anchored, locality-specific disease modification. Full article
(This article belongs to the Special Issue Molecular Mechanisms Underlying Inflammatory Pain)
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16 pages, 2574 KB  
Article
Early Warning of AC Salt Fog Flashover on Composite Insulators Using Fiber Bragg Grating Sensing and Visible Arc Images
by Xiaoxiang Wu, Yanpeng Hao, Zijian Wu, Jikai Bi, Haixin Wu and Lei Huang
Micromachines 2025, 16(10), 1171; https://doi.org/10.3390/mi16101171 - 16 Oct 2025
Abstract
External insulation of coastal power grids faces harsh conditions and is highly susceptible to flashover. Currently, technologies for online monitoring and flashover early warning are severely lacking. As a reflective passive sensing device, Fiber Bragg Grating (FBG) enables the monitoring of surface discharge [...] Read more.
External insulation of coastal power grids faces harsh conditions and is highly susceptible to flashover. Currently, technologies for online monitoring and flashover early warning are severely lacking. As a reflective passive sensing device, Fiber Bragg Grating (FBG) enables the monitoring of surface discharge and provides an early warning for flashover on external insulation. A 10 kV fiber-optic composite insulator was developed in this study. A linear relationship between the FBG central wavelength and interfacial temperature was established through temperature calibration experiments. Coastal salt fog conditions were simulated in an artificial fog chamber, where AC pollution flashover tests were performed on the fiber-optic composite insulator. FBG central wavelength and visible images of discharge were synchronously acquired during experimentation. Experimental results indicate that the interfacial locations on FBGs where the temperature increases during flashover coincide with the positions of visible discharge arcs, demonstrating the effectiveness of the monitoring method. A temperature rise rate of 4.88 × 10−2 °C/s was found to trigger flashover warning, while a rate of 4.96 × 10−2 °C/s initiated trip protection. A discharge-region ratio characteristic was proposed for visible discharge images based on highlight area ratio, R-channel deviation, and mean saturation features. This characteristic serves as a flashover warning when its value reaches 46.7%. This study provides a novel research approach for online monitoring and flashover early warning of external insulation in coastal salt fog environments. Full article
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23 pages, 10902 KB  
Article
Deep Relevance Hashing for Remote Sensing Image Retrieval
by Xiaojie Liu, Xiliang Chen and Guobin Zhu
Sensors 2025, 25(20), 6379; https://doi.org/10.3390/s25206379 - 16 Oct 2025
Abstract
With the development of remote sensing technologies, the volume of remote sensing data is growing dramatically, making efficient management and retrieval of large-scale remote sensing images increasingly important. Recently, deep hashing for content-based remote sensing image retrieval (CBRSIR) has attracted significant attention due [...] Read more.
With the development of remote sensing technologies, the volume of remote sensing data is growing dramatically, making efficient management and retrieval of large-scale remote sensing images increasingly important. Recently, deep hashing for content-based remote sensing image retrieval (CBRSIR) has attracted significant attention due to its computational efficiency and high retrieval accuracy. Although great advancements have been achieved, the imbalance between easy and difficult image pairs during training often limits the model’s ability to capture complex similarities and degrades retrieval performance. Additionally, distinguishing images with the same Hamming distance but different categories remains a challenge during the retrieval phase. In this paper, we propose a novel deep relevance hashing (DRH) for remote sensing image retrieval, which consists of a global hash learning model (GHLM) and a local hash re-ranking model (LHRM). The goal of GHLM is to extract global features from RS images and generate compact hash codes for initial ranking. To achieve this, GHLM employs a deep convolutional neural network to extract discriminative representations. A weighted pairwise similarity loss is introduced to emphasize difficult image pairs and reduce the impact of easy ones during training. The LHRM predicts relevance scores for images that share the same Hamming distance with the query to reduce confusion in the retrieval stage. Specifically, we represent the retrieval list as a relevance matrix and employ a lightweight CNN model to learn the relevance scores of image pairs and refine the list. Experimental results on three benchmark datasets demonstrate that the proposed DRH method outperforms other deep hashing approaches, confirming its effectiveness in CBRSIR. Full article
(This article belongs to the Section Remote Sensors)
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22 pages, 5340 KB  
Article
Circular Array Fiber-Optic Sub-Sensor for Large-Area Bubble Observation, Part I: Design and Experimental Validation of the Sensitive Unit of Array Elements
by Feng Liu, Lei Yang, Hao Li and Zhentao Chen
Sensors 2025, 25(20), 6378; https://doi.org/10.3390/s25206378 - 16 Oct 2025
Abstract
For large-scale measurement of microbubble parameters on the ocean surface beneath breaking waves, a buoy-type bubble sensor (BBS) is proposed. This sensor integrates a panoramic bubble imaging sub-sensor with a circular array fiber-optic sub-sensor. The sensitive unit of the latter sub-sensor is designed [...] Read more.
For large-scale measurement of microbubble parameters on the ocean surface beneath breaking waves, a buoy-type bubble sensor (BBS) is proposed. This sensor integrates a panoramic bubble imaging sub-sensor with a circular array fiber-optic sub-sensor. The sensitive unit of the latter sub-sensor is designed via theoretical modeling and experimental validation. Theoretical calculations indicate that the optimal cone angle for a quartz fiber-optic-based sensitive unit ranges from 45.2° to 92°. A prototype array element with a cone angle of 90° was fabricated and used as the core component for feasibility experiments in static and dynamic two-phase (gas and liquid) identification. During static identification, the reflected optical power differs by an order of magnitude between the two phases. For dynamic sensing of multiple microbubble positions, the reflected optical power varies from 13.4 nW to 29.3 nW, which is within the operating range of the array element’s photodetector. In theory, assembling conical quartz fiber-based sensitive units into fiber-optic probes and configuring them as arrays could overcome the resolution limitations of the panoramic bubble imaging sub-sensor. Further discussion of this approach will be presented in a subsequent paper. Full article
(This article belongs to the Section Optical Sensors)
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22 pages, 6497 KB  
Article
Semantic Segmentation of High-Resolution Remote Sensing Images Based on RS3Mamba: An Investigation of the Extraction Algorithm for Rural Compound Utilization Status
by Xinyu Fang, Zhenbo Liu, Su’an Xie and Yunjian Ge
Remote Sens. 2025, 17(20), 3443; https://doi.org/10.3390/rs17203443 - 15 Oct 2025
Abstract
In this study, we utilize Gaofen-2 satellite remote sensing images to optimize and enhance the extraction of feature information from rural compounds, addressing key challenges in high-resolution remote sensing analysis: traditional methods struggle to effectively capture long-distance spatial dependencies for scattered rural compounds. [...] Read more.
In this study, we utilize Gaofen-2 satellite remote sensing images to optimize and enhance the extraction of feature information from rural compounds, addressing key challenges in high-resolution remote sensing analysis: traditional methods struggle to effectively capture long-distance spatial dependencies for scattered rural compounds. To this end, we implement the RS3Mamba+ deep learning model, which introduces the Mamba state space model (SSM) into its auxiliary branching—leveraging Mamba’s sequence modeling advantage to efficiently capture long-range spatial correlations of rural compounds, a critical capability for analyzing sparse rural buildings. This Mamba-assisted branch, combined with multi-directional selective scanning (SS2D) and the enhanced STEM network framework (replacing single 7 × 7 convolution with two-stage 3 × 3 convolutions to reduce information loss), works synergistically with a ResNet-based main branch for local feature extraction. We further introduce a multiscale attention feature fusion mechanism that optimizes feature extraction and fusion, enhances edge contour extraction accuracy in courtyards, and improves the recognition and differentiation of courtyards from regions with complex textures. The feature information of courtyard utilization status is finally extracted using empirical methods. A typical rural area in Weifang City, Shandong Province, is selected as the experimental sample area. Results show that the extraction accuracy reaches an average intersection over union (mIoU) of 79.64% and a Kappa coefficient of 0.7889, improving the F1 score by at least 8.12% and mIoU by 4.83% compared with models such as DeepLabv3+ and Transformer. The algorithm’s efficacy in mitigating false alarms triggered by shadows and intricate textures is particularly salient, underscoring its potential as a potent instrument for the extraction of rural vacancy rates. Full article
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33 pages, 1124 KB  
Review
Machine and Deep Learning in Agricultural Engineering: A Comprehensive Survey and Meta-Analysis of Techniques, Applications, and Challenges
by Samuel Akwasi Frimpong, Mu Han, Wenyi Zheng, Xiaowei Li, Ernest Akpaku and Ama Pokuah Obeng
Computers 2025, 14(10), 438; https://doi.org/10.3390/computers14100438 - 15 Oct 2025
Abstract
Machine learning and deep learning techniques integrated with advanced sensing technologies have revolutionized agricultural engineering, addressing complex challenges in food production, quality assessment, and environmental monitoring. This survey presents a systematic review and meta-analysis of recent developments by examining the peer-reviewed literature from [...] Read more.
Machine learning and deep learning techniques integrated with advanced sensing technologies have revolutionized agricultural engineering, addressing complex challenges in food production, quality assessment, and environmental monitoring. This survey presents a systematic review and meta-analysis of recent developments by examining the peer-reviewed literature from 2015 to 2024. The analysis reveals computational approaches ranging from traditional algorithms like support vector machines and random forests to deep learning architectures, including convolutional and recurrent neural networks. Deep learning models often demonstrate superior performance, showing 5–10% accuracy improvements over traditional methods and achieving 93–99% accuracy in image-based applications. Three primary application domains are identified: agricultural product quality assessment using hyperspectral imaging, crop and field management through precision optimization, and agricultural automation with machine vision systems. Dataset taxonomy shows spectral data predominating at 42.1%, followed by image data at 26.2%, indicating preference for non-destructive approaches. Current challenges include data limitations, model interpretability issues, and computational complexity. Future trends emphasize lightweight model development, ensemble learning, and expanding applications. This analysis provides a comprehensive understanding of current capabilities and future directions for machine learning in agricultural engineering, supporting the development of efficient and sustainable agricultural systems for global food security. Full article
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20 pages, 8158 KB  
Article
Reconstructing Global Chlorophyll-a Concentration for the COCTS Aboard Chinese Ocean Color Satellites via the DINEOF Method
by Xiaomin Ye, Mingsen Lin, Bin Zou, Xiaomei Wang and Zhijia Lin
Remote Sens. 2025, 17(20), 3433; https://doi.org/10.3390/rs17203433 - 15 Oct 2025
Abstract
The chlorophyll-a (Chl-a) concentration, a critical parameter for characterizing marine primary productivity and ecological health, plays a vital role in providing ecological environment monitoring and climate change assessment while serving as a core retrieval product in ocean color remote sensing. Currently, more than [...] Read more.
The chlorophyll-a (Chl-a) concentration, a critical parameter for characterizing marine primary productivity and ecological health, plays a vital role in providing ecological environment monitoring and climate change assessment while serving as a core retrieval product in ocean color remote sensing. Currently, more than ten ocean color satellites operate globally, including China’s HY-1C, HY-1D and HY-1E satellites. However, significant spatial data gaps exist in Chl-a concentration retrieval from satellites because of cloud cover, sun-glint, and limitation of sensor swath. This study aimed to systematically enhance the spatiotemporal integrity of ocean monitoring data through multisource data merging and reconstruction techniques. We integrated Chl-a concentration datasets from four major sensor types—Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Ocean and Land Color Instrument (OLCI), and Chinese Ocean Color and Temperature Scanner (COCTS)—and quantitatively evaluated their global coverage performance under different payload combinations. The key findings revealed that single-sensor 4-day continuous observation achieved effective coverage levels ranging from only 10.45–26.1%, while multi-sensor merging substantially increased coverage, namely, homogeneous payload merging provided 25.7% coverage for two MODIS satellites, 41.1% coverage for three VIIRS satellites, 24.8% coverage for two OLCI satellites, and 37.1% coverage for three COCTS satellites, with 10-payload merging increasing the coverage rate to 55.4%. Employing the Data Interpolating Empirical Orthogonal Functions (DINEOFS) algorithm, we successfully reconstructed data for China’s ocean color satellites. Validation against VIIRS reconstructions indicated high consistency (a mean relative error of 26% and a linear correlation coefficient of 0.93), whereas self-verification yielded a mean relative error of 27% and a linear correlation coefficient of 0.90. Case studies in Chinese offshore and adjacent waters, waters east of Mindanao Island and north of New Guinea, demonstrated the successful reconstruction of spatiotemporal Chl-a dynamics. The results demonstrated that China’s HY-1C, HY-1D, and HY-1E satellites enable daily global-scale Chl-a reconstruction. Full article
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19 pages, 12813 KB  
Article
Remote Sensing of American Revolutionary War Fortification at Butts Hill (Portsmouth, Rhode Island)
by James G. Keppeler, Marcus Rodriguez, Samuel Koontz, Alexander Wise, Philip Mink, George Crothers, Paul R. Murphy, John K. Robertson, Hugo Reyes-Centeno and Alexandra Uhl
Heritage 2025, 8(10), 430; https://doi.org/10.3390/heritage8100430 - 14 Oct 2025
Abstract
The Battle of Rhode Island in 1778 was an important event in the revolutionary war leading to the international recognition of U.S. American independence following the 1776 declaration. It culminated in a month-long campaign against British forces occupying Aquidneck Island, serving as the [...] Read more.
The Battle of Rhode Island in 1778 was an important event in the revolutionary war leading to the international recognition of U.S. American independence following the 1776 declaration. It culminated in a month-long campaign against British forces occupying Aquidneck Island, serving as the first combined operation of the newly formed Franco-American alliance. The military fortification at Butts Hill in Portsmouth, Rhode Island, served as a strategic point during the conflict and remains well-conserved today. While LiDAR has assisted in the geospatial surface reconstruction of the site’s earthwork fortifications, it is unknown whether other historically documented buildings within the fort remain preserved underground. We therefore conducted a ground-penetrating radar (GPR) survey to ascertain the presence or absence of architectural features, hypothesizing that GPR imaging could reveal structural remnants from the military barracks constructed in 1777. To test this hypothesis, we used public satellite and LiDAR imagery alongside historical maps to target the location of the historical barracks, creating a grid to survey the area with a GPR module in 0.5 m transects. Our results, superimposing remote sensing imagery with historical maps, indicate that the remains of a barracks building are likely present between circa 5–50 cm beneath today’s surface, warranting future investigations. Full article
(This article belongs to the Section Archaeological Heritage)
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