Search Results (16,798)

Wheat is a key staple crop globally, essential for food security and sustainable agricultural development. The results of this study highlight how innovative monitoring techniques, such as UAV-based multispectral imaging, can significantly improve agricultural practices by providing precise, real-time data on crop growth. This study utilized unmanned aerial vehicle (UAV)-based remote sensing technology at the wheat experimental field of the Hebei Academy of Agriculture and Forestry Sciences to capture the dynamic growth characteristics of wheat using multispectral data, aiming to explore efficient and precise monitoring and management strategies for wheat. A UAV equipped with multispectral sensors was employed to collect high-resolution imagery at five critical growth stages of wheat: tillering, jointing, booting, flowering, and ripening. The data covered four key spectral bands: green (560 nm), red (650 nm), red-edge (730 nm), and near-infrared (840 nm). Combined with ground-truth measurements, such as chlorophyll content and plant height, 21 vegetation indices were analyzed for their nonlinear relationships with wheat growth parameters. Statistical analyses, including Pearson’s correlation and stepwise regression, were used to identify the most effective indices for monitoring wheat growth. The Normalized Difference Red-Edge Index (NDRE) and the Triangular Vegetation Index (TVI) were selected based on their superior performance in predicting wheat growth parameters, as demonstrated by their high correlation coefficients and predictive accuracy. A random forest model was developed to comprehensively evaluate the application potential of multispectral data in wheat growth monitoring. The results demonstrated that the NDRE and TVI indices were the most effective indices for monitoring wheat growth. The random forest model exhibited superior predictive accuracy, with a mean squared error (MSE) significantly lower than that of traditional regression models, particularly during the flowering and ripening stages, where the prediction error for plant height was less than 1.01 cm. Furthermore, dynamic analyses of UAV imagery effectively identified abnormal field areas, such as regions experiencing water stress or disease, providing a scientific basis for precision agricultural interventions. This study highlights the potential of UAV-based remote sensing technology in monitoring wheat growth, addressing the research gap in systematic full-cycle analysis of wheat. It also offers a novel technological pathway for optimizing agricultural resource management and improving crop yields. These findings are expected to advance intelligent agricultural production and accelerate the implementation of precision agriculture. Full article

LiDAR is an active remote sensing technology widely used in forestry applications, such as forest resource surveys, tree information collection, and ecosystem monitoring. However, due to the resolution limitations of 3D-laser scanners and the canopy occlusion in forest environments, the tree point clouds obtained often have missing data. This can reduce the accuracy of individual tree segmentation, which subsequently affects the tree species classification. To address the issue, this study used point cloud data with RGB information collected by the UAV platform to improve tree species classification by completing the missing point clouds. Furthermore, the study also explored the effects of point cloud completion, feature selection, and classification methods on the results. Specifically, both a traditional geometric method and a deep learning-based method were used for point cloud completion, and their performance was compared. For the classification of tree species, five machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), Quadratic Discriminant Analysis (QDA), and K-Nearest Neighbors (KNN)—were utilized. This study also ranked the importance of features to assess the impact of different algorithms and features on classification accuracy. The results showed that the deep learning-based completion method provided the best performance (avgCD = 6.14; avgF1 = 0.85), generating more complete point clouds than the traditional method. On the other hand, compared with SVM and BPNN, RF showed better performance in dealing with multi-classification tasks with limited training samples (OA-87.41%, Kappa-0.85). Among the six dominant tree species, Pinus koraiensis had the highest classification accuracy (93.75%), while that of Juglans mandshurica was the lowest (82.05%). In addition, the vegetation index and the tree structure parameter accounted for 50% and 30%, respectively, in the top 10 features in terms of feature importance. The point cloud intensity also had a high contribution to the classification results, indicating that the lidar point cloud data can also be used as an important basis for tree species classification. Full article

Ecological integrity is fundamental to human survival and development. However, rapid urbanization and population growth have significantly disrupted ecosystems. Despite the focus of the International Geosphere-Biosphere Programme (IGBP) on terrestrial ecosystems and land use/cover changes, existing ecological indices, such as the Remote Sensing Ecological Index (RSEI), have limitations, including an overreliance on single indicators and inability to fully encapsulate the ecological conditions of urban areas. This study addresses these gaps by proposing a Deep-learning-based Remote Sensing Ecological Index (DRSEI) that integrates human economic activities and leverages an autoencoder neural network with long short-term memory (LSTM) modules to account for nonlinearity in ecological quality assessments. The DRSEI model utilizes multi-temporal remote sensing data from the Landsat series, WorldPop, and NPP-VIIRS and was applied to evaluate the ecological conditions of Hubei Province, China, over the past two decades. The key findings indicate that ecological environmental quality gradually improved, particularly from 2000 to 2010, with the rate of improvement subsequently slowing. The DRSEI outperformed the traditional RSEI and had a significantly higher Pearson correlation coefficient than the Ecological Index (EI), thus demonstrating enhanced accuracy and predictive performance. This study presents an innovative approach to ecological assessment that offers a more comprehensive, accurate, and nuanced understanding of ecological changes over time. Integrating socioeconomic factors with deep learning techniques contributes significantly to the field and has implications for ecological risk control and sustainable development. Full article

Soil sand content is an important characterization index of soil texture, which directly affects soil water regulation, nutrient cycling, and crop growth potential. Therefore, its high-precision spatial distribution information is of great importance for agricultural resource management and land use. In this study, a remote sensing prediction method based on the combination of time-phase optimization and spectral feature preference is innovatively proposed for improving the mapping accuracy of the sand content in the till layer of a planosol area. The study first analyzed the prediction performance of single-time-phase images, screened the optimal time-phase (May), and constructed a single-time-phase model, which achieved significant prediction accuracy, with a coefficient of determination (R²) of 0.70 and a root mean square error (RMSE) of 1.26%. Subsequently, the model was further optimized by combining multiple time phases, and the prediction accuracy was improved to R² = 0.77 and the RMSE decreased to 1.10%. At the feature level, the recursive feature elimination (RF-RFE) method was utilized to preferentially select 19 key spectral variables from the initial feature set, among which the short-wave infrared bands (b11, b12) and the visible bands (b2, b3, b4) contributed most significantly to the prediction. Finally, the prediction accuracy was further improved to R² = 0.79 and RMSE = 1.05% by multi-temporal-multi-feature fusion modeling. The spatial distribution map of sand content generated by the optimized model shows that areas with high sand content are primarily located in the northern and central regions of Shuguang Farm. This study not only provides a new technical path for accurate mapping of soil texture in the planosol area, but also provides a reference for the improvement of remote sensing monitoring methods in other typical soil areas. The research results can provide a reference for mapping high-resolution soil sand maps over a wider area in the future. Full article

Soil organic matter (SOM) and total nitrogen (TN) are critical indicators for assessing soil fertility. Although laboratory chemical analysis methods can accurately measure their contents, these techniques are time-consuming and labor-intensive. Spectral technology, characterized by its high sensitivity and convenience, has been increasingly integrated with machine learning algorithms for soil nutrient monitoring. However, the process of spectral data analysis remains complex and requires further optimization for simplicity and efficiency to improve prediction accuracy. This study proposes a novel model to enhance the accuracy of SOM and TN predictions in northeast China’s black soil. Visible/Shortwave Near-Infrared Spectroscopy (Vis/SW-NIRS) data within the 350–1070 nm range were collected, preprocessed, and dimensionality-reduced. The scores of the first nine principal components after a partial least squares (PLS) dimensionality reduction were selected as inputs, and the measured SOM and TN contents were used as outputs to build a back-propagation neural network (BPNN) model. The results show that spectral data processed by the combination of standard normal variate (SNV) and multiple scattering correction (MSC) have the best modeling performance. To improve the accuracy and stability of this model, three algorithms named random search (RS), grid search (GS), and Bayesian optimization (BO) were introduced. The results demonstrate that Vis/SW-NIRS provides reliable predictions of SOM and TN contents, with the PLS-RS-BPNN model achieving the best performance (R² = 0.980 and 0.972, RMSE = 1.004 and 0.006 for SOM and TN, respectively). Compared to traditional models such as random forests (RF), one-dimensional convolutional neural networks (1D-CNNs), and extreme gradient boosting (XGBoost), the proposed PLS-RS-BPNN model improves R² by 0.164–0.344 in predicting SOM and by 0.257–0.314 in predicting TN, respectively. These findings confirm the potential of Vis/SW-NIRS technology and the PLS-RS-BPNN model as effective tools for soil nutrient prediction, offering valuable insights for the application of spectral technology in sensing soil information. Full article

A Mach–Zehnder interferometer-based tapered-in-tapered fiber-optic biosensor was introduced in this paper. By integrating a micro-tapered fiber into a single tapered fiber structure, the design enhances sensitivity, signal-to-noise ratio, and resolution capability, while reducing the length of the sensing fiber. Through simulation analysis, it was found that the tapered-in-tapered fiber significantly improved the refractive index detection sensitivity by exciting a stronger evanescent field effect. The experimental comparison between the tapered-in-tapered fiber and traditional tapered fiber showed a 1.7-fold increase in sensitivity, reaching 3266.78 nm/RIU within the refractive index range of 1.3326 to 1.3414. Furthermore, to expand its application prospects in the biomedical field, glutaraldehyde cross-linking technology was used to immobilize C-reactive protein (CRP) antibodies on the surface of the tapered-in-tapered fiber, successfully creating a biosensing platform for the specific recognition of CRP. The experimental results demonstrate that this novel biosensor can rapidly and accurately detect CRP molecules at different concentrations with a detection limit of 0.278 μg/mL, and that it exhibits good selectivity and repeatability. This tapered-in-tapered fiber-optic biosensor provides new insights into the development of high-performance fiber-optic immunosensors and shows broad application potential in immunology research and early disease diagnosis. Full article

Soil, a crucial natural resource and the cornerstone of agriculture, profoundly impacts crop growth, quality, and yield. However, soil degradation affects over one-third of global land, with topsoil loss emerging as a significant form of this degradation, posing a grave threat to agricultural sustainability and socio-economic development. Therefore, accurate monitoring of topsoil-loss distribution is essential for formulating effective soil protection and management strategies. Traditional survey methods are limited by time-consuming and labor-intensive processes, high costs, and complex data processing. These limitations make it particularly challenging to meet the demands of large-scale research and efficient information processing. Therefore, it is imperative to develop a more efficient and accurate extraction method. This study focuses on the Heshan Farm in Heilongjiang Province, China, as the research subject and utilizes remote sensing technology and machine learning methods. It introduces multi-source data, including Sentinel-2 satellite imagery and Digital Elevation Model (DEM) data, to design four extraction schemes. (1) spectral feature extraction; (2) spectral feature + topographic feature extraction; (3) spectral feature + index extraction; (4) spectral feature + topographic feature + index extraction. Models for topsoil loss identification based on Random Forest (RF) and Support Vector Machine (SVM) algorithms are developed, and the Particle Swarm Optimization (PSO) algorithm is introduced to optimize the models. The performance of the models is evaluated using overall accuracy and Kappa coefficient indicators. The results show that Scheme 4, which integrates spectral features, topographic features, and various indices, performs the best in extraction effects. The RF model demonstrates higher classification accuracy than the SVM model. The optimized PSO-RF and PSO-SVM models show significant improvements in extraction accuracy, especially the PSO-RF model, with an overall accuracy of 0.97 and a Kappa coefficient of 0.94. The PSO-RF model using Scheme 4 improves OA by 34.72% and Kappa by 38.81% compared to the RF model in Scheme 1. Topsoil loss has a significant negative impact on crop growth, severely restricting the normal growth and development of crops. This study provides an efficient technical means for monitoring soil degradation in black-soil regions and offers a scientific basis for formulating effective agricultural ecological protection strategies, thereby promoting the sustainable management of soil resources. Full article

This study employed a full-scale cement sheath quality evaluation apparatus, along with a high-precision distributed fiber optic temperature sensing system, to perform real-time, continuous monitoring of the temperature change throughout the cement hydration process. The results of the cement annulus and cement bond defect monitoring during the hydration process indicated that the distributed fiber optic temperature data enabled centimeter-level resolution in defect identification. Defective regions exhibited significantly reduced temperature fluctuation amplitudes, and an inversion in temperature change at the early hydration stage, detected at the cement–defect boundary, facilitated the early detection of defect locations. The distributed fiber optic system was capable of conducting continuous and comprehensive monitoring of the sequential hydration temperature peaks of cement stages injected into the annulus. The results revealed the interdependence among different cement stages, as well as a phenomenon whereby an elevated annular temperature accelerates the progression of cement hydration. The experimental findings provide a reference for identifying the characteristic signals in distributed fiber optic monitoring of well-cementing operations, thereby establishing a foundation for the optimal and effective use of distributed fiber optics in assessing well-cementing quality. Full article

With the advancement of remote sensing technology, the acquisition of ultra-high-resolution remote sensing imagery has become a reality, opening up new possibilities for detailed research and applications of Earth’s surface. These ultra-high-resolution images, with spatial resolutions at the meter or sub-meter level and pixel counts exceeding 4 million, contain rich geometric and attribute details of surface objects. Their use significantly improves the accuracy of surface feature analysis. However, this also increases the computational resource demands of deep learning-driven semantic segmentation tasks. Therefore, we propose the Transform Dual-Branch Attention Net (TDBAN), which effectively integrates global and local information through a dual-branch design, enhancing image segmentation performance and reducing memory consumption. TDBAN leverages a cross-collaborative module (CCM) based on the Transform mechanism and a data-related learnable fusion module (DRLF) to achieve adaptive content processing. Experimental results show that TDBAN achieves mean intersection over union (mIoU) of 73.6% and 72.7% on DeepGlobe and Inria Aerial datasets, respectively, and surpasses existing models in memory efficiency, highlighting its superiority in handling ultra-high-resolution remote sensing images. This study not only advances the development of ultra-high-resolution remote sensing image segmentation technology, but also lays a solid foundation for further research in this field. Full article

Free-standing ferroelectric films have emerged as a transformative technology in the field of flexible electronics, offering unique properties that enable a wide range of applications, including sensors, actuators, and energy harvesting devices. This review paper explores recent advancements in the fabrication, characterization, and application of free-standing ferroelectric films, highlighting innovative techniques such as multilayer structures and van der Waals epitaxy that enhance their performance while maintaining mechanical flexibility. We discuss the critical role of these films in next-generation devices, emphasizing their potential for integration into multifunctional systems that combine energy harvesting and sensing capabilities. Additionally, we address challenges related to leakage currents, polarization stability, and scalability that must be overcome to facilitate commercialization. By synthesizing current research findings and identifying future directions, this paper aims to provide a comprehensive overview of the state-of-the-art in free-standing ferroelectric films and their impact on the development of sustainable and efficient flexible electronic technologies. Full article

A capacitive six-axis force/torque (F/T) sensor has favorable characteristics for miniature design. However, when designing small-sized force/torque sensors, anisotropy among the six axes can lead to uneven sensitivity across each axis. This is due to increased crosstalk errors, which degrade sensor performance. To design a miniature six-axis force/torque sensor, it is essential to analyze the isotropic relationships between the six-axis forces/torques and the capacitance change to reduce crosstalk errors. This paper presents a miniature capacitive six-axis F/T sensor optimized for isotropy. It also establishes a systematic method for designing sensing electrodes. The sensor’s deformable structure is analyzed using Castigliano’s beam theory, and design parameters are optimized with isotropy analysis of the deformable part. The criteria are also presented, including selecting the electrode area and initial gap using linear equations derived from capacitance change analysis. The optimized miniature F/T sensor is calibrated using a neural network-based calibration method, and its accuracy errors are compared to a reference sensor. The design framework provides a foundation for future developments in miniature sensors. Full article

Integrating Internet of Things (IoT) devices and machine learning (ML) techniques holds immense potential for transforming beekeeping practices. This review paper offers a critical analysis of state-of-the-art IoT-enabled precision beekeeping systems. It examines the diverse sensor technologies deployed for honeybee data acquisition, delving into their strengths and limitations, particularly regarding accuracy, reliability, energy sustainability, transmission range, feasibility, and scalability. Furthermore, this paper dissects prevalent ML models used for bee behaviour analysis, disease detection, and colony monitoring tasks. This paper evaluates their methodologies, performance metrics, and the challenges involved in selecting appropriate machine learning algorithms. It also examines the influence of sensing devices, computational complexity, dataset limitations, validation procedures, evaluation metrics, and the effects of pre-processing techniques on these models’ outcomes. Building upon this analysis, this paper identifies key research gaps and proposes promising avenues for future investigation. The focus is on the synergistic use of IoT and ML to address colony health management challenges and the overall sustainability of the beekeeping industry. Full article

Despite the introduction of preventive safety measures, such as near-miss reporting, to mitigate accidents and minimize fatalities, construction workers are constantly exposed to stressful situations that negatively affect their safety behavior and reporting efficiency. Occupational stress is induced by various factors, with mental stress and auditory stress being common workplace stressors that impact workers on the job site. While previous studies have demonstrated the effect of stressor conditions on workers’ hazard recognition and safety performance, research gaps persist regarding the direct impact of workplace stressors on workers’ stress levels and near-miss recognition performance. This study investigates workers’ near-miss recognition ability through an eye-tracking experiment conducted in a controlled environment under mental and auditory stress conditions. The findings from this study reveal that workplace stressors triggered by mental and auditory stress can adversely affect worker stress levels, safety behavior, and cognitive processing toward near-miss recognition. Visual attention towards near-miss scenarios was reduced by 26% for mental stress conditions and by 46% for auditory stress conditions compared to baseline. The results may potentially open avenues for developing wearable stress prediction and safety intervention models using bio-sensing technology and personalized safety training programs tailored to individuals with low identification abilities. Full article

Organic field-effect transistors (OFETs) are an ideal platform for intrinsically stretchable sensors due to their diverse mechanisms and unique electrical signal amplification characteristics. The remarkable advantages of intrinsically stretchable sensors lie in their molecular tunability, lightweight design, mechanical robustness, solution processability, and low Young’s modulus, which enable them to seamlessly conform to three-dimensional curved surfaces while maintaining electrical performance under significant deformations. Intrinsically stretchable sensors have been widely applied in smart wearables, electronic skin, biological detection, and environmental protection. In this review, we summarize the recent progress in intrinsically stretchable sensors based on OFETs, including advancements in functional layer materials, sensing mechanisms, and applications such as gas sensors, strain sensors, stress sensors, proximity sensors, and temperature sensors. The conclusions and future outlook discuss the challenges and future outlook for stretchable OFET-based sensors. Full article

Beach surface moisture (BSM) is crucial to studying coastal aeolian sand transport processes. However, traditional measurement techniques fail to accurately monitor moisture distribution with high spatiotemporal resolution. Remote sensing technologies have garnered widespread attention for providing rapid and non-contact moisture measurements, but a single method has inherent limitations. Passive remote sensing is challenged by complex beach illumination and sediment grain size variability. Active remote sensing represented by LiDAR (light detection and ranging) exhibits high sensitivity to moisture, but requires cumbersome intensity correction and may leave data holes in high-moisture areas. Using machine learning, this research proposes a BSM inversion method that fuses UAV (unmanned aerial vehicle) orthophoto brightness with intensity recorded by TLSs (terrestrial laser scanners). First, a back propagation (BP) network rapidly corrects original intensity with in situ scanning data. Second, beach sand grain size is estimated based on the characteristics of the grain size distribution. Then, by applying nearest point matching, intensity and brightness data are fused at the point cloud level. Finally, a new BP network coupled with the fusion data and grain size information enables automatic brightness correction and BSM inversion. A field experiment at Baicheng Beach in Xiamen, China, confirms that this multi-source data fusion strategy effectively integrates key features from diverse sources, enhancing the BP network predictive performance. This method demonstrates robust predictive accuracy in complex beach environments, with an RMSE of 2.63% across 40 samples, efficiently producing high-resolution BSM maps that offer values in studying aeolian sand transport mechanisms. Full article