Search Results (1,232)

Citrus diseases severely impact fruit yield and quality. To facilitate in-field, non-destructive, and rapid detection of citrus leaf physiological and pathological conditions, this study proposes a classification method for citrus leaf physiological and pathological statuses that integrates visible/near-infrared multispectral technology with deep learning. First, a handheld spectrometer was employed to acquire spectral images of five sample categories—Healthy, Huanglongbing, Yellow Vein Disease, Magnesium Deficiency and Manganese Deficiency. Mean spectral data were extracted from regions of interest within the 350–2500 nm wavelength range, and various preprocessing techniques were evaluated. The Standard Normal Variate (SNV) transformation, which demonstrated optimal performance, was selected for data preprocessing. Next, we innovatively introduced an adaptive spectral positional encoding mechanism into the Transformer framework. A lightweight, learnable network dynamically optimizes positional biases, yielding the ASTransformer (Adaptive Spectral Transformer) model, which more effectively captures complex dependencies among spectral features and identifies critical wavelength bands, thereby significantly enhancing the model’s adaptive representation of discriminative bands. Finally, the preprocessed spectra were fed into three deep learning architectures (1D-CNN, 1D-ResNet, and ASTransformer) for comparative evaluation. The results indicate that ASTransformer achieves the best classification performance: an overall accuracy of 97.7%, underscoring its excellent global classification capability; a Macro Average of 97.5%, reflecting balanced performance across categories; a Weighted Average of 97.8%, indicating superior performance in classes with larger sample sizes; an average precision of 97.5%, demonstrating high predictive accuracy; an average recall of 97.7%, showing effective detection of most affected samples; and an average F1-score of 97.6%, confirming a well-balanced trade-off between precision and recall. Furthermore, interpretability analysis via Integrated Gradients quantitatively assesses the contribution of each wavelength to the classification decisions. These findings validate the feasibility of combining a handheld spectrometer with the ASTransformer model for effective citrus leaf physiological and pathological detection, enabling efficient classification and feature visualization, and offer a valuable reference for disease detection of physiological and pathological conditions in other fruit crops. Full article

Amid the health-conscious consumption trend, functional foods rich in γ-aminobutyric acid (GABA) and vitamin B9 are gaining prominence. Foxtail millet, a traditional grain naturally abundant in these nutrients, faces quality assessment challenges due to the time-consuming and destructive nature of conventional methods, hindering large-scale screening. This study pioneers the systematic application of hyperspectral imaging (HSI) for nondestructive detection of GABA and vitamin B9 in millet. Utilizing spectral data from 190 samples across 19 varieties, we developed an innovative “coarse-fine” feature wavelength selection strategy. First, interval-based algorithms (iRF, iVISSA) screened highly correlated wavelength subsets. Second, model population analysis (MPA) algorithms (CARS, BOSS) identified optimal core wavelengths, boosting model efficiency and robustness. Based on this, a stacked BiLSTM–Adaboost model was built, integrating bidirectional long short-term memory networks for sequence dependency and adaptive boosting for enhanced generalization. This enables efficient, rapid, nondestructive, and precise nutrient detection. This interdisciplinary breakthrough establishes a novel pathway for millet nutritional assessment, deepens fundamental research, and provides core support for industrial upgrading, breeding, quality control, and functional food development, supporting national health. Full article

Accurate and timely assessment of crop disease severity is crucial for effective management strategies and ensuring sustainable agricultural production. Traditional visual disease scoring methods are subjective and labor-intensive, highlighting the need for automated, objective alternatives. This study evaluates the effectiveness of a model for field-based identification and quantification of stripe rust severity in wheat using red, green, blue RGB imaging. Based on crop reflectance hyperspectra (CRHS) acquired using a FieldSpec ASD spectroradiometer, two complementary approaches were developed. In the first approach, we estimate single leaf disease severity (LDS) under laboratory conditions, while in the second approach, we assess crop disease severity (CDS) from field-based RGB images. The high accuracy of both methods enabled the development of a predictive model for estimating LDS from CDS, offering a scalable solution for precision disease monitoring in wheat cultivation. The experiment was conducted on four winter wheat plots subjected to varying fungicide treatments to induce different levels of stripe rust severity for model calibration, with treatment regimes ranging from no application to three applications during the growing season. RGB images were acquired in both laboratory conditions (individual leaves) and field conditions (nadir and oblique perspectives), complemented by hyperspectral measurements in the 350–2500 nm range. To achieve automated and objective assessment of disease severity, we developed custom image-processing scripts and applied Random Forest classification and regression models. The models demonstrated high predictive performance, with the combined use of nadir and oblique RGB imagery achieving the highest classification accuracy (97.87%), sensitivity (100%), and specificity (95.83%). Oblique images were more sensitive to early-stage infection, while nadir images offered greater specificity. Spectral feature selection revealed that wavelengths in the visible (e.g., 508–563 nm and 621–703 nm) and red-edge/SWIR regions (around 1556–1767 nm) were particularly informative for disease detection. In classification models, shorter wavelengths from the visible range proved to be more useful, while in regression models, longer wavelengths were more effective. The integration of RGB-based image analysis with the Random Forest algorithm provides a robust, scalable, and cost-effective solution for monitoring stripe rust severity under field conditions. This approach holds significant potential for enhancing precision agriculture strategies by enabling early intervention and optimized fungicide application. Full article

We propose a novel method for controlling the frequency of semiconductor lasers. This technique facilitates the production of devices with fast frequency tuning and intrinsic linearization of laser frequency sweeps. The electrical contact layer positioned between the lower undoped cladding and the waveguide, along with the upper laser contact, is used for the optical gain pumping. Since the laser pumping current does not pass through the lower cladding, changes in carrier concentration within the cladding affect the laser frequency while minimally impacting the device’s output power. Control of the free carrier concentration in the lower cladding is achieved using the space-charge-limited current (SCLC) technique. This novel approach establishes a linear relationship between the laser frequency shift (∆f) and voltage (V) applied to the cladding—an essential feature for light detection and ranging (LIDAR) system development. The proposed technique is applicable to all semiconductor lasers. As an example, we present the calculated characteristics of a quantum cascade laser (QCL) operating at a 10 µm wavelength. Full article

Focusing on the characteristic tea resource Zijuan tea, this study addresses the difficulty of grading on production lines and the complexity of quality evaluation. On the basis of the fusion of near-infrared (NIR) spectroscopy and visual features, a novel method is proposed for classifying different tenderness levels and quantitatively assessing key anthocyanin components in Zijuan tea fresh leaves. First, NIR spectra and visual feature data were collected, and anthocyanin components were quantitatively analyzed using UHPLC-Q-Exactive/MS. Then, four preprocessing techniques and three wavelength selection methods were applied to both individual and fused datasets. Tenderness classification models were developed using Particle Swarm Optimization–Support Vector Machine (PSO-SVM), Random Forest (RF), and Convolutional Neural Networks (CNNs). Additionally, prediction models for key anthocyanin content were established using linear Partial Least Squares Regression (PLSR), nonlinear Support Vector Regression (SVR) and RF. The results revealed significant differences in NIR spectral characteristics across different tenderness levels. Model combinations such as TEX + Medfilt + RF and NIR + Medfilt + CNN achieved 100% accuracy in both training and testing sets, demonstrating robust classification performance. The optimal models for predicting key anthocyanin contents also exhibited excellent predictive accuracy, enabling the rapid and nondestructive detection of six major anthocyanin components. This study provides a reliable and efficient method for intelligent tenderness classification and the rapid, nondestructive detection of key anthocyanin compounds in Zijuan tea, holding promising potential for quality control and raw material grading in the specialty tea industry. Full article

To determine the structure of carbohydrate polymers using conventional analytical technology, several complicated steps are required. We instead adopted a direct approach without the need for pretreatments, using an intense infrared (IR) laser for carbohydrate analysis. IR free-electron lasers (FELs) driven by a linear accelerator possess unique spectroscopic features, including extensive wavelength tunability and high laser energy in the IR region from 1000 cm⁻¹ (10 μm) to 4000 cm⁻¹ (2.5 μm). FELs can induce IR multiphoton dissociation reactions against various molecules by supplying vibrational excitation energy to the corresponding chemical bonds. Chitin from crayfish and cellulose fiber were irradiated by FELs tuned to νC–O (9.1–9.8 μm), νC–H (3.5 μm), and δH–C–O (7.2 μm) in glycosidic bonds, and their low-molecular-weight sugars were separated, which were revealed by combining synchrotron radiation IR spectroscopy and electrospray ionization mass spectrometry. An intense IR laser can be viewed as a “molecular scalpel” for dissecting and directly analyzing the internal components in rigid biopolymers. This method is simple and rapid compared with general analytical techniques. Full article

Wavelength modulation-tunable diode laser absorption spectroscopy (WM-TDLAS) is a critical tool for gas detection. However, noise in second harmonic signals degrades detection performance. This study presents a hybrid denoising algorithm combining Empirical Mode Decomposition (EMD) and wavelet adaptive thresholding to enhance WM-TDLAS performance. The algorithm decomposes raw signals into intrinsic mode functions (IMFs) via EMD, selectively denoises high-frequency IMFs using wavelet thresholding, and reconstructs the signal while preserving spectral features. Simulation and experimental validation using the CH₄ absorption spectrum at 1654 nm demonstrate that the system achieves a threefold improvement in detection precision (0.1181 ppm). Allan variance analysis revealed that the detection capability of the system was significantly enhanced, with the minimum detection limit (MDL) drastically reduced from 2.31 ppb to 0.53 ppb at 230 s integration time. This approach enhances WM-TDLAS performance without hardware modification, offering significant potential for environmental monitoring and industrial safety applications. Full article

In this work, a novel polarization-maintaining hollow-core fiber structure featuring a semi-circular nested dual-ring geometry is proposed. To simultaneously optimize two inherently conflicting performance metrics, namely, birefringence and confinement loss, a multi objective genetic algorithm is employed for geometric parameter tuning, resulting in a set of Pareto-optimal solutions. At the target wavelength of 1550 nm, the first optimal design achieves birefringence exceeding $1\times {10}^{-4}$ over a 1275 nm bandwidth while maintaining confinement loss around ${10}^0$ dB/m; the second design maintains birefringence above $1\times {10}^{-4}$ across a 1000 nm spectral range, with confinement loss on the order of ${10}^{-1}$ dB/m. These optimized designs offer a promising approach for improving the performance of polarization-sensitive applications such as interferometric sensing and high coherence laser systems. The results confirm the suitability of multi-objective genetic algorithms for integrated multi-objective fiber optimization and provide a new strategy for designing low-loss and high-birefringence fiber devices. Full article

To exploit the near-infrared (NIR) light of MoO₃, the MoVO_x mixed oxide was synthesized using a one-pot approach. The effects of different electrodes, V doping, and bias on the optoelectronic properties were investigated. The photoelectric responses to light sources with wavelengths of 405, 532, 650, 780, 808, 980, and 1064 nm were studied using both Au and carbon electrodes with 6B pencil drawings. The results demonstrate that the MoVO_x nanoblets exhibit photocurrent switching characteristics across the broadband region of the light spectrum. Even when zero bias was applied and the mixed oxide sample was stored at room temperature for over two years, a good photoelectric signal was still observed. This demonstrates that the MoVO_x nanoblets present an interface where interfacial charge transfer forms a strong built-in electric field, promoting photogenerated charge separation and transfer while suppressing photogenerated carrier recombination, and exhibiting self-powered characteristics. Interestingly, reducing the power of the typical excitation light sources resulted in a transition from positive to negative photocurrent features. This reflects the result of an imbalance between the concentration of material defects and the concentration of photogenerated electrons. The MoVO_x nanoblets not only enhance charge transport performance, but also significantly improve the exploitation of near-infrared light. Doping with V significantly improves the nanocomposites’ near-infrared (NIR) photoelectric sensitivity. This study demonstrates that heavily doping aliovalent ions during the in situ preparation of nanocomposites effectively enhances their photophysical properties. It provides a straightforward approach to narrowing the band gap of wide-bandgap oxides and effectively avoiding the recombination of photogenerated carriers. Full article

The online rapid classification of multi-cultivar watermelon, including seedless and seeded types, has far-reaching significance for enhancing quality control in the watermelon industry. However, interference in one-dimensional spectra affects the high-accuracy classification of multi-cultivar watermelons with similar appearances. This study proposed an innovative method integrating Gramian Angular Field (GAF), feature fusion, and Squeeze-and-Excitation (SE)-guided convolutional neural networks (CNN) based on VIS-NIR transmittance spectroscopy. First, one-dimensional spectra of 163 seedless and 160 seeded watermelons were converted into two-dimensional Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF) images. Subsequently, a dual-input CNN architecture was designed to fuse discriminative features from both GASF and GADF images. Feature visualization of high-weight channels of the input images in convolutional layer revealed distinct spectral features between seedless and seeded watermelons. With the fusion of distinguishing feature information, the developed CNN model achieved a classification accuracy of 95.1% on the prediction set, outperforming traditional models based on one-dimensional spectra. Remarkably, wavelength optimization through competitive adaptive reweighted sampling (CARS) reduced GAF image generation time to 55.19% of full-wavelength processing, while improving classification accuracy to 96.3%. A better generalization of the model was demonstrated using 17 seedless and 20 seeded watermelons from other origins, with a classification accuracy of 91.9%. These findings substantiated that GAF-enhanced feature fusion CNN can significantly improve the classification accuracy of multi-cultivar watermelons, casting innovative light on fruit quality based on VIS-NIR transmittance spectroscopy. Full article

Conventional radiative transfer (RT) models often adopt the plane-parallel (PP) approximation, which neglects Earth’s curvature and leads to significant optical path errors under large solar or sensor zenith angles, particularly for high-latitude regions and twilight conditions. The spherical Monte Carlo method offers high accuracy but is computationally expensive, and the commonly used pseudo-spherical (PSS) approximation fails when the viewing zenith angle exceeds 80°. With the increasing application of machine learning in atmospheric science, the efficiency and angular limitations of spherical RT simulations may be overcome. This study provides a physical and quantitative foundation for developing a hybrid RT framework that integrates physical modeling with machine learning. By systematically quantifying the discrepancies between PP and spherical RT models under diverse atmospheric scenarios, key influencing factors—including wavelength, solar and viewing zenith angles, aerosol properties (e.g., single scattering albedo and asymmetry factor), and PP-derived radiance—were identified. These variables significantly affect spherical radiative transfer and serve as effective input features for data-driven models. Using the corresponding spherical radiance as the target variable, the proposed framework enables rapid and accurate inference of spherical radiative outputs based on computationally efficient PP simulations. Full article

Efficient and convenient intelligent online detection methods can provide important technical support for the standardization of processing flow in the tea industry. Hence, this study focuses on the key chemical indicators—tea pigments in the rolling process of black tea as the research object, and uses multi-source information fusion methods to predict the changes of tea pigments content. Firstly, the tea pigments content of the samples under different rolling time series of black tea is determined by system analysis methods. Secondly, the spectra and images of the corresponding samples under different rolling time series are simultaneously obtained through the portable near-infrared spectrometer and the machine vision system. Then, by extracting the principal components of the image feature information and screening characteristic wavelengths from the spectral information, low-level and middle-level data fusion strategies are chosen to effectively integrate sensor data from different sources. At last, the linear (PLSR) and nonlinear (SVR and LSSVR) models are established respectively based on the different characteristic data information. The research results show that the LSSVR based on middle-level data fusion strategy have the best effect. In the prediction results of theaflavins, thearubigins, and theabrownins, the correlation coefficients of the testing sets are all greater than 0.98, and the relative percentage deviations are all greater than 5. The complementary fusion of the spectrum and image information effectively compensates for the problems of information redundancy and feature missing in the quantitative analysis of tea pigments content using the single-modal data information. Full article

Anti-resonant hollow-core fibers have exhibited excellent performance in applications such as high-power pulse transmission, network communication, space exploration, and precise sensing. Employing anti-resonant hollow-core fibers instead of light guiding arms for transmitting laser energy at the 2.79 μm band can significantly enhance the flexibility of medical laser handles, reduce system complexity, and increase laser transmission efficiency. Nevertheless, common anti-resonant hollow-core fibers do not have the ability to maintain the polarization state of light during laser transmission, which greatly affects their practical applications. In this paper, we propose a polarization-maintaining anti-resonant hollow-core fiber applicable for transmission at the mid-infrared 2.79 μm band. This fiber features a symmetrical geometric structure and an asymmetric refractive index cladding composed of quartz and a type of mid-infrared glass with a higher refractive index. Through optimizing the fiber structure at the wavelength scale, single-polarization transmission can be achieved at the 2.79 μm wavelength, with a polarization extinction ratio exceeding 1.01 × 10⁵, indicating its stable polarization-maintaining performance. Simultaneously, it possesses low-loss transmission characteristics, with the loss in the x-polarized fundamental mode being less than 9.8 × 10⁻³ dB/m at the 2.79 µm wavelength. This polarization-maintaining anti-resonant hollow-core fiber provides a more reliable option for the light guiding system of the 2.79 μm Er; Cr: YSGG laser therapy device. Full article

In this work, the feasibility of simulating the thermal inactivation of Brochothrix thermosphacta in beef during heating processing based on hyperspectral imaging (HSI) in the wavelength range of 400–1000 nm was investigated. The Weibull and modified Gompertz kinetic models for the thermal inactivation of B. thermosphacta in beef heated in the range 40–60 °C were developed based on the full wavelength, featured spectral variables, and their principal component scores of HSI information, respectively. Notably, the specific wavebands at 412 nm and 735 nm showed a strong correlation with the surviving B. thermosphacta population during the beef heating process. The partial least squares regression models had a satisfactory ability in quantifying B. thermosphacta in beef, with an R_v² and RMSE of 0.826 and 0.341 log CFU/g, respectively. Furthermore, the Weibull model coupled with the HSI at 735 nm was suitable for kinetic modeling of the thermal inactivation of B. thermosphacta in beef, with an R² value of 0.937. Consequently, this work suggests the potential of the HSI technique for quantifying and monitoring microbes in meat during heating and can be applied for the thermal inactivation kinetic modeling of microorganisms. Full article

Underwater image enhancement (UIE) is a key technology in the fields of underwater robot navigation, marine resources development, and ecological environment monitoring. Due to the absorption and scattering of different wavelengths of light in water, the quality of the original underwater images usually deteriorates. In recent years, UIE methods based on deep neural networks have made significant progress, but there still exist some problems, such as insufficient local detail recovery and difficulty in effectively capturing multi-scale contextual information. To solve the above problems, a Multi-Scale Contextual Fusion Residual Network (MCFR-Net) for underwater image enhancement is proposed in this paper. Firstly, we propose an Adaptive Feature Aggregation Enhancement (AFAE) module, which adaptively strengthens the key regions in the input images and improves the feature expression ability by fusing multi-scale convolutional features and a self-attention mechanism. Secondly, we design a Residual Dual Attention Module (RDAM), which captures and strengthens features in key regions through twice self-attention calculation and residual connection, while effectively retaining the original information. Thirdly, a Multi-Scale Feature Fusion Decoding (MFFD) module is designed to obtain rich contexts at multiple scales, improving the model’s understanding of details and global features. We conducted extensive experiments on four datasets, and the results show that MCFR-Net effectively improves the visual quality of underwater images and outperforms many existing methods in both full-reference and no-reference metrics. Compared with the existing methods, the proposed MCFR-Net can not only capture the local details and global contexts more comprehensively, but also show obvious advantages in visual quality and generalization performance. It provides a new technical route and benchmark for subsequent research in the field of underwater vision processing, which has important academic and application values. Full article