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Silvicultural practices, particularly planting density (PD), significantly affect the wood quality of plantation-grown trees by influencing the age of transition from corewood to outerwood. Determining transition age (TA) is challenging. Near infrared hyperspectral imaging was used to assess the age of transition from corewood to outerwood in Douglas-fir (Pseudotsuga menziesii) grown under different PD. Wood samples were taken from permanent research plots with six PD ranging from 2,990 to 248 stems/hectare. Twenty trees were destructively sampled at age 25, and wood discs were collected at 10 % of total tree height. Pith-to-bark radial strips obtained from each disc were scanned using a compovision hyperspectral imaging system (1,002–2,350 nm). Partial least square discriminant analysis (PLS-DA) models were fitted to approximate TA using hyperspectral data representative of corewood and outerwood. PD influenced TA and the duration over which it occurred. Average age of transition was estimated to vary from 10 (closest spacing) to 14 years (widest). Closely spaced trees exhibited an abrupt transition, while widely spaced trees had a more gradual transition. However, PLS-DA likely underestimated the transition duration for widely spaced trees, as their average ring densities did not yet match those typical of outerwood based on X-ray densitometry data. [ABSTRACT FROM AUTHOR]

Hyperspectral images are high-dimensional data containing rich spatial, spectral, and radiometric information, widely used in geological mapping, urban remote sensing, and other fields. However, due to the characteristics of hyperspectral remote sensing images—such as high redundancy, strong correlation, and large data volumes—the classification and recognition of these images present significant challenges. In this paper, we propose a band selection method (GE-AP) based on multi-feature extraction and the Affine Propagation Clustering (AP) algorithm for dimensionality reduction of hyperspectral images, aiming to improve classification accuracy and processing efficiency. In this method, texture features of the band images are extracted using the Gray-Level Co-occurrence Matrix (GLCM), and the Euclidean distance between bands is calculated. A similarity matrix is then constructed by integrating multi-feature information. The AP algorithm clusters the bands of the hyperspectral images to achieve effective band dimensionality reduction. Through simulation and comparison experiments evaluating the overall classification accuracy (OA) and Kappa coefficient, it was found that the GE-AP method achieves the highest OA and Kappa coefficient compared to three other methods, with maximum increases of 8.89% and 13.18%, respectively. This verifies that the proposed method outperforms traditional single-information methods in handling spatial and spectral redundancy between bands, demonstrating good adaptability and stability. [ABSTRACT FROM AUTHOR]

AbstractThe breeding of silkworms is a crucial step in silkworm rearing. To breed high quality hybrid silkworm species, it is necessary to obtain silkworm pupae of different sex and species with high purity. This study explores the feasibility of simultaneously identifying the sex and species of silkworm pupae using hyperspectral imaging (HSI) technology. Firstly, a total of 400 hyperspectral images of 4 species of silkworm pupae were acquired using the hyperspectral imaging system, and the spectral information within the wavelength range of 400.89–1002.19 nm was extracted. The original spectral data was preprocessed using Savitzky-Golay (SG) smoothing combined with multivariate scattering correction (MSC), and a combined dimensionality reduction method of PCA-t-SNE was constructed to reduce the dimensionality of spectral data, aiming to retain the global and local features of the original spectra as much as possible. Subsequently, multiple classifiers were utilized to achieve the simultaneous identification of the sex and species of silkworm pupae. Among them, classification accuracies of the test set, utilizing PCA-t-SNE combined dimensionality reduction on the WHO-SVM and CNN models, achieve 93.75% and 95%, respectively. The results indicate that the combined dimensionality reduction method of PCA-t-SNE can extract representative spectral information more effectively and improve the classification accuracy. Simultaneously, the CNN model with strong learning ability has great potential in the classification of silkworm pupae. In conclusion, HSI technology can be effectively utilized for the identification of silkworm pupae to accomplish high-precision and complex classification tasks. [ABSTRACT FROM AUTHOR]

The inhomogeneity of spectral pixel response is an unavoidable phenomenon in hyperspectral imaging, which is mainly manifested by the existence of inhomogeneity banding noise in the acquired hyperspectral data. It must be carried out to get rid of this type of striped noise since it is frequently uneven and densely distributed, which negatively impacts data processing and application. By analyzing the source of the instrument noise, this work first created a novel non-uniform noise removal method for a spatial dimensional push sweep hyperspectral imaging system. Clean and clear medical hyperspectral brain tumor tissue images were generated by combining scene-based and reference-based non-uniformity correction denoising algorithms, providing a strong basis for further diagnosis and classification. The precise procedure entails gathering the reference dark background image for rectification and the actual medical hyperspectral brain tumor image. The original hyperspectral brain tumor image is then smoothed using a weighted least squares algorithm model embedded with bilateral filtering (BLF-WLS), followed by a calculation and separation of the instrument fixed-mode fringe noise component from the acquired reference dark background image. The purpose of eliminating non-uniform fringe noise is achieved. In comparison to other common image denoising methods, the evaluation is based on the subjective effect and unreferenced image denoising evaluation indices. The approach discussed in this paper, according to the experiments, produces the best results in terms of the subjective effect and unreferenced image denoising evaluation indices (MICV and MNR). The image processed by this method has almost no residual non-uniform noise, the image is clear, and the best visual effect is achieved. It can be concluded that different denoising methods designed for different noises have better denoising effects on hyperspectral images. The non-uniformity denoising method designed in this paper based on a spatial dimension push-sweep hyperspectral imaging system can be widely used. [ABSTRACT FROM AUTHOR]

Background: This research proposes an easy to apply quality assurance pipeline for hyperspectral imaging (HSI) systems used for plant phenotyping. Furthermore, a concept for the analysis of quality assured hyperspectral images to investigate plant disease progress is proposed. The quality assurance was applied to a handheld line scanning HSI-system consisting of evaluating spatial and spectral quality parameters as well as the integrated illumination. To test the spatial accuracy at different working distances, the sine-wave-based spatial frequency response (s-SFR) was analysed. The spectral accuracy was assessed by calculating the correlation of calibration-material measurements between the HSI-system and a non-imaging spectrometer. Additionally, different illumination systems were evaluated by analysing the spectral response of sugar beet canopies. As a use case, time series HSI measurements of sugar beet plants infested with Cercospora leaf spot (CLS) were performed to estimate the disease severity using convolutional neural network (CNN) supported data analysis. Results: The measurements of the calibration material were highly correlated with those of the non-imaging spectrometer (r>0.99). The resolution limit was narrowly missed at each of the tested working distances. Slight sharpness differences within individual images could be detected. The use of the integrated LED illumination for HSI can cause a distortion of the spectral response at 677nm and 752nm. The performance for CLS diseased pixel detection of the established CNN was sufficient to estimate a reliable disease severity progression from quality assured hyperspectral measurements with external illumination. Conclusion: The quality assurance pipeline was successfully applied to evaluate a handheld HSI-system. The s-SFR analysis is a valuable method for assessing the spatial accuracy of HSI-systems. Comparing measurements between HSI-systems and a non-imaging spectrometer can provide reliable results on the spectral accuracy of the tested system. This research emphasizes the importance of evenly distributed diffuse illumination for HSI. Although the tested system showed shortcomings in image resolution, sharpness, and illumination, the high spectral accuracy of the tested HSI-system, supported by external illumination, enabled the establishment of a neural network-based concept to determine the severity and progression of CLS. The data driven quality assurance pipeline can be easily applied to any other HSI-system to ensure high quality HSI. [ABSTRACT FROM AUTHOR]

In order to achieve precise discrimination of leaf diseases in the Maize/Soybean intercropping system, i.e. leaf spot disease, rust disease, mixed leaf diseases, this study utilized hyperspectral imaging and deep learning algorithms for the classification of diseased leaves of maize and soybean. In the experiments, hyperspectral imaging equipment was used to collect hyperspectral images of leaves, and the regions of interest were extracted within the spectral range of 400 to 1000 nm. These regions included one or more infected areas on the leaves to obtain hyperspectral data. This approach aimed to enhance the accurate discrimination of different types of diseases, providing more effective technical support for the detection and control of crop diseases. The preprocessing of hyperspectral data involved four methods: Savitzky-Golay (SG), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC) and 1st Derivative (1st Der). The 1st Der was found to be the optimal preprocessing method for hyperspectral data of maize and soybean diseases. Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA) and Principal Component Analysis (PCA) were employed for feature extraction on the optimal preprocessed data. The Support Vector Machines (SVM), Bidirectional Long Short-Term Memory Network (BiLSTM) and Dung Beetle Optimization-Bidirectional Long Short-Term Memory Network (DBO-BiLSTM) were established for the discrimination of maize and soybean diseases. Comparative analysis indicated that, in the classification of maize and soybean diseases, the DBO-BiLSTM model based on the CARS extraction method (1st Der-CARS-DBO-BiLSTM) demonstrated the highest classification rate, reaching 98.7% on the test set. The research findings suggest that integrating hyperspectral imaging with both traditional and deep learning methods is a viable and effective approach for classifying diseases in the intercropping model of maize and soybean. These results offer a novel method and a theoretical foundation for the non-invasive, precise, and efficient identification of diseases in the intercropping model of maize and soybean, carrying positive implications for agricultural production. [ABSTRACT FROM AUTHOR]

Introduction: The early detection of esophageal cancer is crucial to enhancing patient survival rates, and endoscopy remains the gold standard for identifying esophageal neoplasms. Despite this fact, accurately diagnosing superficial esophageal neoplasms poses a challenge, even for seasoned endoscopists. Recent advancements in computer-aided diagnostic systems, empowered by artificial intelligence (AI), have shown promising results in elevating the diagnostic precision for early-stage esophageal cancer. Methods: In this study, we expanded upon traditional red–green–blue (RGB) imaging by integrating the YOLO neural network algorithm with hyperspectral imaging (HSI) to evaluate the diagnostic efficacy of this innovative AI system for superficial esophageal neoplasms. A total of 1836 endoscopic images were utilized for model training, which included 858 white-light imaging (WLI) and 978 narrow-band imaging (NBI) samples. These images were categorized into three groups, namely, normal esophagus, esophageal squamous dysplasia, and esophageal squamous cell carcinoma (SCC). Results: An additional set comprising 257 WLI and 267 NBI images served as the validation dataset to assess diagnostic accuracy. Within the RGB dataset, the diagnostic accuracies of the WLI and NBI systems for classifying images into normal, dysplasia, and SCC categories were 0.83 and 0.82, respectively. Conversely, the HSI dataset yielded higher diagnostic accuracies for the WLI and NBI systems, with scores of 0.90 and 0.89, respectively. Conclusion: The HSI dataset outperformed the RGB dataset, demonstrating an overall diagnostic accuracy improvement of 8%. Our findings underscored the advantageous impact of incorporating the HSI dataset in model training. Furthermore, the application of HSI in AI-driven image recognition algorithms significantly enhanced the diagnostic accuracy for early esophageal cancer. [ABSTRACT FROM AUTHOR]

Cloud geometry and in particular cloud top heights can be derived from 2-D camera measurements by applying a stereographic method to data from an overflight over a scene of clouds see, e.g.,. Although airplane overpasses are relatively fast, cloud motion with the wind is important and can result in errors in the cloud localization. Here, the impact of the wind is investigated using the method from for measurements of the airborne hyperspectral imaging system spectrometer of the Munich Aerosol Cloud Scanner (specMACS). Further, a method for cloud motion correction using model winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) is presented. It is shown that the update is important as the original algorithm without the cloud motion correction can over- or underestimate the cloud top heights by several hundred meters, depending on the wind speed and the relative wind direction. This is validated using data from the Elucidating the role of clouds–circulation coupling in climate (EUREC 4 A) campaign and realistic 3-D radiative transfer simulations. From the comparison of the derived cloud top heights with the expected ones from the model input, an average accuracy of the cloud top heights of less than (20±140) m (mean deviation and 1 standard deviation) is estimated for the updated method. [ABSTRACT FROM AUTHOR]

Hyperspectral Imaging (HSI) has revolutionized earth observation through advanced remote sensing technology, providing rich spectral and spatial information across multiple bands. However, this wealth of data introduces significant challenges for classification, including high spectral correlation, the curse of dimensionality due to limited labeled data, the need to model long-term dependencies, and the impact of sample input on deep learning performance. These challenges are further exacerbated by the costly and complex acquisition of HSI data, resulting in limited availability of labeled samples and class imbalances. To address these critical issues, our study proposes a novel approach for generating high-quality synthetic hyperspectral data cubes using an advanced Generative Adversarial Network (GAN) integrated with the Wasserstein loss and gradient penalty phenomenon (WGAN-GP). This approach aims to augment real-world data, mitigating the scarcity of labeled samples that has long been a bottleneck in hyperspectral image analysis and classification. To fully leverage both the synthetic and real data, we introduce a novel Convolutional LSTM classifier designed to process the intricate spatial and spectral correlations inherent in hyperspectral data. This classifier excels in modeling multi-dimensional relationships within the data, effectively capturing long-term dependencies and improving feature extraction and classification accuracy. The performance of our proposed model, termed 3D-ACWGAN-ConvLSTM, is rigorously validated using benchmark hyperspectral datasets, demonstrating its effectiveness in augmenting real-world data and enhancing classification performance. This research contributes to addressing the critical need for robust data augmentation techniques in hyperspectral imaging, potentially opening new avenues for applications in areas constrained by limited data availability and complex spectral-spatial relationships. [ABSTRACT FROM AUTHOR]

Laser-induced breakdown spectroscopy (LIBS) has the potential to serve as a valuable tool in the field of metal failure estimation. In this work, 12Cr1MoV steel, a material with different grain size grades, was selected as the experimental sample. Spectral and acoustic data were recorded during the laser ablation process. Initially, it was revealed that the acoustic energy did not exhibit a significant downward trend with the continuous laser shots, but the acoustic energy fluctuations became more intense. In order to enhance the capacity to assess the grain size grade of heat-resistant steel, we advanced a novel proposition to integrate acoustic data with spectral data. Two data fusion strategies were proposed for the integration of spectral and acoustic data: first, dimensionality reduction followed by combination, and second, combination followed by dimensionality reduction. Subsequently, two classification models, linear discriminant analysis (LDA) and support vector machines (SVM), were constructed utilising three data types: spectral data, acoustic spectral data, and the aforementioned combined data set. The performance of the model trained on the combined data obtained based on the first strategy is superior to models trained on a single data type (spectral data or acoustic spectral data), achieving a classification accuracy of 92.29%. The second strategy yielded unsatisfactory results due to the significant difference in dimensions between spectral data and acoustic spectral data. To address this, a modification was proposed by carrying out spectral feature screening on spectra data using RFE before data fusion and studying the impact of the number of remaining variables after RFE processing on model performance. The results showed that the model achieved the highest classification accuracy of 98.85%. The measurement illustrates the effectiveness of integrating spectral and acoustic spectral data for enhanced metal assessment. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to intensive genetic selection for rapid growth rates and high broiler yields in recent years, the global poultry industry has faced a challenging problemin the form of woody breast(WB) conditions. This condition has caused significant economic losses as high as $200 million annually, and the root cause of WB has yet to be identified. Human palpation is the most common method of distinguishing a WB from others. However, this method is time-consuming and subjective. Hyperspectral imaging (HSI) combined with machine learning algorithms can evaluate the WB conditions of fillets in a non-invasive, objective, and high-throughput manner. In this study, 250 raw chicken breast fillet samples (normal, mild, severe) were taken, and spatially heterogeneous hardness distribution was first considered when designing HSI processing models. The study not only classified the WB levels from HSI but also built a regression model to correlate the spectral information with sample hardness data. To achieve a satisfactory classification and regression model, a neural network architecture search (NAS) enabled a wide-deep neural network model named NAS-WD, which was developed. In NAS-WD, NAS was first used to automatically optimize the network architecture and hyperparameters. The classification results show that NAS-WD can classify the threeWBlevels with an overall accuracy of 95 %, outperforming the traditionalmachine learning model, and the regression correlation between the spectral data and hardness was 0.75, which performs significantly better than traditional regression models. [ABSTRACT FROM AUTHOR]

Tomatoes (Solanum lycopersicum L.) are a widely grown and globally traded vegetable, essential for both local consumption and international trade. However, approximately 30% of harvested tomato yields are lost due to fungal decay during postharvest handling. Timely disease identification is crucial to prevent such losses, but certain tomato varieties exhibit higher susceptibility to fungal infections than others. Additionally, there are variations in susceptibility among individual sepals, with unknown underlying causes. Traditional methods for assessing fungal presence in plants have limitations, such as sample destruction and a focus on symptom detection rather than evaluating susceptibility to fungal infection. Hence, there is a demand need for an accurate, non-destructive method capable of predicting susceptibility to fungal infection. The use of hyperspectral imaging (HSI) with chemometrics presents a pioneering approach to address this need. In this study, three tomato cultivars ('Brioso,' 'Cappricia,' and 'Provine') were studied. Hyperspectral images were captured on day-1 of harvest, followed by controlled fungal growth conditions. Ground truth assessments were conducted by three experts on day-3 and day-4, averaging severity scores assigned per sepal. The methodology involved extracting spectra from HSI images and calibrating and validating models using partial least squares discriminant analysis (PLSDA), aiming to optimize model parameters for accurate predictions. The models were categorized into those developed using data from a single variety (intravariety) and those utilizing data from multiple varieties combined (global models). The best-performing intravariety model was established using the Cappricia variety, achieving a balanced accuracy of 0.84. Conversely, a global model combining Cappricia and Provine varieties achieved a balanced accuracy of 0.70. Overall, the results suggest that distinguishing between more and less susceptible sepals is feasible under controlled conditions. [ABSTRACT FROM AUTHOR]

The moisture content of fabrics directly affects the physical properties of fabrics, it is important to accurately detect the moisture content of fabrics. To verify the feasibility of hyperspectral imaging technology for detecting the moisture content of cotton fabrics and thus improve the accuracy of moisture content detection, 100 groups of hyperspectral images of cotton fabrics with different thicknesses and moisture contents were acquired, the characteristics of the spectral curves of cotton fabrics were elaborated, and the effects of thickness and moisture content on the spectral curves of cotton fabrics were analyzed. Based on the research above, 1190, 1450 and 1940 nm bands were determined as the characteristic bands, the content gradient method was selected to divide the training set and sample set, PLS (Partial Least Squares) prediction models of the water content with MSC (Multiplicative Scatter Correction), SNV (Standard Normal Variate) and the first order derivative spectral pretreatment methods were established respectively, and the accuracy of the established three models was compared by correlation coefficient and standard error finally. The following conclusions were drawn: the change of water content in cotton fabric not only affects the overall reflectance of the cotton fabric spectral curve but also changes the position and peak of the characteristic peaks; with the increase of water content, the proportion of pure water in the mixed spectra of cotton fabric containing water increases, the position of the characteristic peaks is shifted and the peak increases; for cotton fabrics, the final spectral curves is the overlap of the water NIR (Near Infrared) and cotton absorption band at the position; the correlation coefficients of the training set and the test set of the established PLS-First Order Derivative model reached 0.98239 and 0.97637, the standard errors are 0.0003 and 0.00041, which proves the feasible of the method of utilizing hyperspectral imaging system with the prediction model for quantitative moisture content inspection of cotton fabrics. [ABSTRACT FROM AUTHOR]

Copyright of World Nuclear Geoscience is the property of World Nuclear Geoscience Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In head and neck surgery, continuous intraoperative tissue differentiation is of great importance to avoid injury to sensitive structures such as nerves and vessels. Hyperspectral imaging (HSI) with neural network analysis could support the surgeon in tissue differentiation. A 3D Convolutional Neural Network with hyperspectral data in the range of 400 − 1000 nm is used in this work. The acquisition system consisted of two multispectral snapshot cameras creating a stereo-HSI-system. For the analysis, 27 images with annotations of glandular tissue, nerve, muscle, skin and vein in 18 patients undergoing parotidectomy are included. Three patients are removed for evaluation following the leave-one-subjectout principle. The remaining images are used for training, with the data randomly divided into a training group and a validation group. In the validation, an overall accuracy of 98.7% is achieved, indicating robust training. In the evaluation on the excluded patients, an overall accuracy of 83.4% has been achieved showing good detection and identification abilities. The results clearly show that it is possible to achieve robust intraoperative tissue differentiation using hyperspectral imaging. Especially the high sensitivity in parotid or nerve tissue is of clinical importance. It is interesting to note that vein was often confused with muscle. This requires further analysis and shows that a very good and comprehensive data basis is essential. This is a major challenge, especially in surgery. [ABSTRACT FROM AUTHOR]

China ranks first in apple production worldwide, making the assessment of apple quality a critical factor in agriculture. Sucrose concentration (SC) is a key factor influencing the flavor and ripeness of apples, serving as an important quality indicator. Nondestructive SC detection has significant practical value. Currently, SC is mainly measured using handheld refractometers, hydrometers, electronic tongues, and saccharimeter analyses, which are not only time-consuming and labor-intensive but also destructive to the sample. Therefore, a rapid nondestructive method is essential. The fluorescence hyperspectral imaging system (FHIS) is a tool for nondestructive detection. Upon excitation by the fluorescent light source, apples displayed distinct fluorescence characteristics within the 440–530 nm and 680–780 nm wavelength ranges, enabling the FHIS to detect SC. This study used FHIS combined with machine learning (ML) to predict SC at the apple's equatorial position. Primary features were extracted using variable importance projection (VIP), the successive projection algorithm (SPA), and extreme gradient boosting (XGBoost). Secondary feature extraction was also conducted. Models like gradient boosting decision tree (GBDT), random forest (RF), and LightGBM were used to predict SC. VN-SPA + VIP-LightGBM achieved the highest accuracy, with R p 2 , R M S E p , and R P D reaching 0.9074, 0.4656, and 3.2877, respectively. These results underscore the efficacy of FHIS in predicting apple SC, highlighting its potential for application in nondestructive quality assessment within the agricultural sector. [ABSTRACT FROM AUTHOR]

The demand for achromatic ultrathin flat lenses has become increasingly stringent, particularly for high‐performance imaging and display applications. Despite significant progress in achromatic metasurface and diffraction lenses, no single material has yet been capable of constructing ultrathin achromatic flat lenses covering ultrabroad wavebands, including the visible and near‐infrared (NIR), due to the limitations of material bandgaps. This limitation complicates fabrication processes, integration, and miniaturization, often leading to instability. In this paper, making use of the dispersionless nature of graphene, high numerical aperture multi‐wavelength achromatic metalenses (MAGLs) made entirely from graphene is proposed and demonstrated. This approach, based on a partial intensity resonance (PIR) mechanism, requires no iterative algorithms. Two MAGLs for visible and communication bands, respectively, are designed and fabricated. Remarkably, the measured focal lengths only deviate by less than 0.15% from the desired values. The graphene metalens (GML) in the visible produced clear and high‐quality images of microscopic character and Brassica napus cells. The demonstrated MAGLs significantly simplify the fabrication process and enhance integration, miniaturization, and stability. Their unique single‐material design offers tremendous potential to replace conventional refractive lenses in applications such as virtual reality glasses, hyperspectral imaging systems, and fluorescence microscopes. [ABSTRACT FROM AUTHOR]

Early detection of plum bruising is important in the online postharvest quality sorting process. This paper explores the rapid detection of bruises in plum at five stages (1, 3, 6, 24, and 48 h after bruising) using hyperspectral imaging system. Spectral preprocessing was performed using five methods (unit vector normalization, multiplicative scattering correction, standard normal variate, detrending, and baseline). The support vector machine was established to discriminate the spectral samples of healthy and bruised plums at stage 1 on full wavelengths. The results indicated that the spectral data pretreated by multiple scattering correction yielded better results. The characteristic wavelengths of the spectra were selected by six algorithms (random frog, competitive adaptive reweighted sampling, variable combination population analysis, successive projection algorithm, uninformative variable elimination and bootstrapping soft shrinkage). Subsequently, the support vector machine was established at stage 1 based on these selected characteristic wavelengths. Comparing the results of models, the support vector machine based on the characteristic wavelengths selected by competitive adaptive reweighted sampling generated a satisfied effect with an accuracy of 95% for the calibration set and 98% for the prediction set. This model was selected for bruise detection in plums at the residual stage. The characteristic wavelength grayscale images selected based on the model were used to successfully visualize the plum bruise regions in five stages using minimum noise fraction transformation and the principal component analysis method. The results showed that the hyperspectral imaging technique combined with machine learning algorithm could be used to identify plum bruises at different stages. This study contributes to the development of an online detection system for bruises in plum. [ABSTRACT FROM AUTHOR]

The presence of gray mold can seriously affect the yield and quality of strawberries. Due to their susceptibility and the rapid spread of this disease, it is important to develop early, accurate, rapid, and non-destructive disease identification strategies. In this study, the early detection of strawberry leaf diseases was performed using hyperspectral imaging combining multi-dimensional features like spectral fingerprints and vegetation indices. Firstly, hyperspectral images of healthy and early affected leaves (24 h) were acquired using a hyperspectral imaging system. Then, spectral reflectance (616) and vegetation index (40) were extracted. Next, the CARS algorithm was used to extract spectral fingerprint features (17). Pearson correlation analysis combined with the SPA method was used to select five significant vegetation indices. Finally, we used five deep learning methods (LSTMs, CNNs, BPFs, and KNNs) to build disease detection models for strawberries based on individual and fusion characteristics. The results showed that the accuracy of the recognition model based on fused features ranged from 88.9% to 96.6%. The CNN recognition model based on fused features performed best, with a recognition accuracy of 96.6%. Overall, the fused feature-based model can reduce the dimensionality of the classification data and effectively improve the predicting accuracy and precision of the classification algorithm. [ABSTRACT FROM AUTHOR]

Pest infestations significantly impact rice production and threaten food security. Remote sensing offers a vital tool for the non-destructive, rapid detection of rice pests. Existing studies often focus on laboratory conditions at the leaf level, limiting their applicability for precise pesticide application. Therefore, this study aimed to develop a method for detecting rice pests (rice leaf folders) in paddy fields based on unmanned aerial vehicle (UAV) hyperspectral data. Firstly, a UAV imaging system collected hyperspectral images of rice plants in both the jointing and heading stages. A total of 222 field plots for investigating rice leaf folders was established during these two periods. Secondly, 23 vegetation indices were calculated as candidates for identifying rice pests. Then, hyperspectral data and field investigation data from the jointing stage were used to construct a machine learning (extreme gradient boosting, XGBoost) algorithm for detecting rice pests. The results showed that the XGBoost model exhibited the best performance when eight vegetation indices were considered as the selected input features for model construction: the Red-edge Normalized Difference Vegetation Index (red-edge NDVI), Structure Insensitive Pigment Index (SIPI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI), Soil-Adjusted Vegetation Index (SAVI), Red-edge Chlorophyll Index (CIred-edge), Pigment-Specific Simple Ratio680 (PSSR680), and Carotenoid Reflectance Index700 (CPI700). The training and testing accuracies reached 87.46% and 86%, respectively. Furthermore, the heading stage application confirmed the model's feasibility. Thus, the XGBoost model with input features of eight vegetation indices provides an effective and reliable method for detecting rice leaf folders, supporting real-time, precise pesticide use in rice cultivation. [ABSTRACT FROM AUTHOR]

Rosmarinic acid (RA) is a phenolic antioxidant naturally occurring in the plants of the Lamiaceae family, including basil (Ocimum basilicum L.). Existing analytical methods for determining the RA content in leaves are time-consuming and destructive, posing limitations on quality assessment and control during cultivation. In this study, we aimed to develop non-destructive prediction models for the RA content in basil plants using a portable hyperspectral imaging (HSI) system and machine learning algorithms. The basil plants were grown in a vertical farm module with controlled environments, and the HSI of the whole plant was captured using a portable HSI camera in the range of 400–850 nm. The average spectra were extracted from the segmented regions of the plants. We employed several spectral data pre-processing methods and ensemble learning algorithms, such as Random Forest, AdaBoost, XGBoost, and LightGBM, to develop the RA prediction model and feature selection based on feature importance. The best RA prediction model was the LightGBM model with feature selection by the AdaBoost algorithm and spectral pre-processing through logarithmic transformation and second derivative. This model performed satisfactorily for practical screening with R2P = 0.81 and RMSEP = 3.92. From in-field HSI data, the developed model successfully estimated and visualized the RA distribution in basil plants growing in the greenhouse. Our findings demonstrate the potential use of a portable HSI system for monitoring and controlling pharmaceutical quality in medicinal plants during cultivation. This non-destructive and rapid method can provide a valuable tool for assessing the quality of RA in basil plants, thereby enhancing the efficiency and accuracy of quality control during the cultivation stage. [ABSTRACT FROM AUTHOR]

Color is a key indicator for evaluating the quality of tea during processing; various processing procedures can significantly affect the content of fat-soluble pigments of tea, which in turn affects the color and quality of finished tea. Therefore, there is an urgent demand for the fast, non-destructive detection of pigments of stacked tea during processing. This paper presents the use of hyperspectral imaging technology (HSI), combined with machine learning algorithms, to detect chlorophyll a, chlorophyll b, and carotenoids in stacked matcha tea during processing. Firstly, a quantitative relationship between HSI data of tea and their pigment contents was developed based on regression analysis, and the results showed that exceptional prediction performance was achieved by the partial least squares regression (PLSR) algorithm combined with the feature band algorithm of competitive adaptive reweighting (CARS), and the Rp2 values of detection models of chlorophyll a, chlorophyll b and carotenoids were 0.90465, 0.92068 and 0.62666, respectively. Then, these quantitative detection models were extended to each pixel in hyperspectral images, achieving point-by-point prediction of pigment components, so the distribution of pigments of stacked tea leaves during processing procedures was successfully visualized on the processing line in situ. By integrating a hyperspectral imaging system into the real-world environment, operators can monitor pigment levels in real time and thus dynamically adjust processing parameters based on real-time data. This study enhances pigment detection efficiency in tea processing, supports process optimization, and aids in quality control. [ABSTRACT FROM AUTHOR]

Remote sensing is a powerful technique for classifying and quantifying objects. However, the elaborate classification of objects in coastal waters with complex structures is still challenging due to the high possibility of class mixing. The classification through the hyperspectral images can be a reasonable alternative to problems related to such precise classification work because it has high spectral resolution over a wide bandwidth. This study introduced the results of the case study using a novel method to classify green algae on an artificial structure based on hyperspectral data and deep-learning models. The spectral characteristics of the attached green algae on the artificial structure were observed using a ground-based hyperspectral camera. The observed image had a total of three classes (concrete, dense green algae, and sparse green algae). A certain area of the image was used as learning data to create classification models for three classes. The classification models were created from one machine-learning (support vector machine, SVM) and two deep-learning models (convolutional neural network, CNN; and dense convolutional network, DenseNet). As a result, the performance for the classification results of green algae predicted from two deep-learning models was higher than that of the machine-learning model. Additionally, the deep-learning model successfully classified the interface area between concrete and green algae. This study suggests that the combination of hyperspectral data and deep learning could enable more precise classification of objects in coastal areas. [ABSTRACT FROM AUTHOR]

Goji berry is acknowledged for its notable medicinal attributes and elevated free radical scavenger properties. Nevertheless, its susceptibility to mechanical injuries and biological disorders reduces the commercial diffusion of the fruit. A hyperspectral imaging system (HSI) was employed to identify common defects in the Vis-NIR range (400–1000 nm). The sensorial evaluation of visual appearance was used to obtain the reference measurement of defects. A supervised classification model employing PLS-DA was developed using raw and pre-processed spectra, followed by applying a covariance selection algorithm (CovSel). The classification model demonstrated superior performance in two classifications distinguishing between sound and defective fruit, achieving an accuracy and sensitivity of 94.9% and 96.9%, respectively. However, when extended to a more complex task of classifying fruit into four categories, the model exhibited reliable results with an accuracy and sensitivity of 74.5% and 77.9%, respectively. These results indicate that a method based on hyperspectral visible-NIR can be implemented for rapid and reliable methods of online quality inspection securing high-quality goji berries. [ABSTRACT FROM AUTHOR]

Pasta enriched with soy flour can be considered as a functional food, due to its content in nutraceutical compounds such as isoflavones, carotenoids, and other antioxidants. The quantification of the amount of a functional ingredient is an important step in food authenticity. The availability of non-destructive techniques for quantitative and qualitative analyses of food is therefore desirable. This research aimed to investigate the feasibility of hyperspectral imaging in reflectance mode for the evaluation of the soy flour content, also to investigate the possibility of implementing a feed-back control system to precisely dose the soy flour during the industrial production of pasta. Samples of pasta in shape of spaghetti were produced with durum wheat semolina and soy flour at increasing percentages (0, to 50%, steps of 5%). A feature selection algorithm was used to predict the amount of soy flour. The most influent wavelengths were selected, and a six-term Gauss function was trained, validated, and tested. The identified transfer function was able to predict the percentage of soy flour with high accuracy, with an R2adj value of 0.98 and a Root Mean Square Error of 1.31. The developed system could represent a feasible tool to control the process in a continuous mode. [ABSTRACT FROM AUTHOR]

Techniques that exploit spectral-spatial information have proven to be very effective in hyperspectral image classification. Joint sparse representation classification (JSRC) is one such technique which has been extensively used for this purpose. However, the use of a single fixed-sized window has limited its ability to incorporate spatial information. Several techniques such as multiscale superpixels based sparse representation classification (MSSRC), multiscale adaptive sparse representation classification (MASRC) and Discriminant Subdictionary Learning (DSDL) have tried to overcome this drawback by fusing information from different scales. However, their inability to simultaneously consider the correlated information at different scales and appropriate spatial neighbourhoods limits their performance. In order to better model contextual information, in this paper, we propose a modified max-tree and modified min-tree to represent the connected components of the image. Then, by exploiting these connected components, adaptive multiscale windows are defined. The potentiality of the proposed technique is validated by performing a comparative analysis with four state-of-the-art sparse representation methods using three real hyperspectral datasets. For a fixed training and test sets of University of Pavia and Indian Pines dataset, our proposed technique provides at least 3% and 2%, respectively higher classification results than the best state-of-the-art method. [ABSTRACT FROM AUTHOR]

Acid value (AV) serves as an important indicator to assess the quality of oil, which can be used to judge the deterioration of edible oil. In order to realize the quantitative prediction of the AV of camellia seed oil, which was made from camellia oleifolia, hyperspectral data of 168 camellia seed oil samples were collected using a hyperspectral imaging system, which were related to their AV content measured via classical chemical titration. On the basis of hyperspectral full wavelengths, characteristic wavelengths, and fusing spectral and image features, the quantitative prediction AV models for camellia seed oil were established. The results demonstrating the 2Der-SPA-GLCM-PLSR model fusing spectral and image features stood out as the optimal choices for the AV prediction of camellia seed oil, with the correlation coefficient of calibration set ( R c 2 ) and the correlation coefficient of prediction set ( R p 2 ) at 0.9698 and 0.9581, respectively. Compared with those of 2Der-SPA-PLSR, the R c 2 and R p 2 were improved by 2.11% and 2.57%, respectively. Compared with those of 2Der-PLSR, the R c 2 and R p 2 were improved by 5.02% and 5.31%, respectively. Compared with the model based on original spectrum, the R c 2 and R p 2 were improved by 32.63% and 40.11%, respectively. After spectral preprocessing, characteristic wavelength selection, and fusing spectral and image features, the correlation coefficient of the optimal AV prediction model was continuously improved, while the root mean square error was continuously decreased. The research demonstrated that hyperspectral imaging technology could precisely and quantitatively predict the AV of camellia seed oil and also provide a new environmental method for detecting the AV of other edible oils, which is conducive to sustainable development. [ABSTRACT FROM AUTHOR]

Background: The automation of pest monitoring is highly important for enhancing integrated pest management in practice. In this context, advanced technologies are becoming increasingly explored. Hyperspectral imaging (HSI) is a technique that has been used frequently in recent years in the context of natural science, and the successful detection of several fungal diseases and some pests has been reported. Various automated measures and image analysis methods offer great potential for enhancing monitoring in practice. Results: In this study, the use of hyperspectral imaging over a wide spectrum from 400 to 2500 nm is investigated for noninvasive identification and the distinction of healthy plants and plants infested with Myzus persicae (Sulzer) and Frankliniella occidentalis (Pergande) on bell peppers. Pest infestations were carried out in netted areas, and images of single plants and dissected leaves were used to train the decision algorithm. Additionally, a specially modified spraying robot was converted into an autonomous platform used to carry the hyperspectral imaging system to take images under greenhouse conditions. The algorithm was developed via the XGBoost framework with gradient-boosted trees. Signals from specific wavelengths were found to be associated with the damage patterns of different insects. Under confined conditions, M. persicae and F. occidentalis infestations were distinguished from each other and from the uninfested control for single leaves. Differentiation was still possible when small whole plants were used. However, application under greenhouse conditions did not result in a good fit compared to the results of manual monitoring. Conclusion: Hyperspectral images can be used to distinguish sucking pests on bell peppers on the basis of single leaves and intact potted bell pepper plants under controlled conditions. Wavelength reduction methods offer options for multispectral camera usage in high-grown vegetable greenhouses. The application of automated platforms similar to the one tested in this study could be possible, but for successful pest detection under greenhouse conditions, algorithms should be further developed fully considering real-world conditions. [ABSTRACT FROM AUTHOR]

To address the challenge of non-destructive detection of sucrose solution application in the tobacco leaf processing stage, a discrimination model for sucrose solution application based on hyperspectral imaging and machine learning had been developed. Hyperspectral data of tobacco leaf samples with varying sucrose solution applications were first acquired using a visible-shortwave infrared hyperspectral imaging system and preprocessed with standard normal variate (SNV). Four discrimination models for sucrose solution application were then constructed and validated using full-spectrum data and principal component analysis (PCA) reduced data, in conjunction with support vector machine (SVM), logistic regression (LR), multilayer perceptron (MLP), and random forest (RF). The results showed that SNV preprocessing significantly enhanced the feature concentration of the hyperspectral data. When modeling with full-spectrum data, the models in the shortwave infrared band demonstrated significantly higher prediction accuracy compared to those in the visible light band, with the LR model in the shortwave infrared band achieving the highest accuracy of 98.23%. Compared to full-spectrum data modeling, the prediction accuracy of models using the top 10 principal components from PCA reduced data showed little change in the shortwave infrared band, while the RF model's accuracy in the visible light band improved significantly to 71.43%. In the visible light band, the highest accuracy for PCA-reduced data models corresponded to 217, 55, 47, and 59 principal components, while in the shortwave infrared band, the numbers were 13, 11, 117, and 46, respectively. Overall, LR and RF models exhibited superior predictive perf ormance, with the LR model based on PCA-reduced data in the shortwave infrared band maintaining high accuracy with fewer principal components, demonstrating the capability for rapid, non-destructive, and precise determination of sucrose solution application on tobacco leaves. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging has appeared as a vital tool in remote sensing science for its efficacy in effectively delineating regions of interest. However, the classification of hyperspectral images (HSI) encounters notable challenges, including the high dimensionality of highly correlated bands and the scarcity of training samples. Addressing these challenges is very essential by determining the most relevant bands, as well as the utilization of unlabelled training samples. In response to these issues, this study presents an unsupervised framework based on an enhanced Mayfly Optimization Algorithm (MOA) in order to select the most informative spectral bands. The enhanced MOA effectively identifies informative bands by leveraging the random solutions to explore the global search space, and enhance the solution diversity. On the other hand, leveraging the best experiences to boost the local search, efficiently attaining optimal solutions. This balanced exploration-exploitation strategy ensures the algorithm's robustness and effectiveness in addressing the optimization problem. Ultimately, the proposed approach is demonstrated at the pixel-level hyperspectral image classification using two machine learning classifiers: Random Forest and Support Vector Machine. Thorough experimentation carried out on three benchmark hyperspectral datasets consistently confirms the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

As the primary grain crop in China, wheat holds a significant position in the country's agricultural production, circulation, consumption, and various other aspects. However, the presence of imperfect grains has greatly impacted wheat quality and, subsequently, food security. In order to detect perfect wheat grains and six types of imperfect grains, a method for the fast and non-destructive identification of imperfect wheat grains using hyperspectral images was proposed. The main contents and results are as follows: (1) We collected wheat grain hyperspectral data. Seven types of wheat grain samples, each containing 300 grains, were prepared to construct a hyperspectral imaging system for imperfect wheat grains, and visible near-infrared hyperspectral data from 2100 wheat grains were collected. The Savitzky–Golay algorithm was used to analyze the hyperspectral images of wheat grains, selecting 261 dimensional effective hyperspectral datapoints within the range of 420.61–980.43 nm. (2) The Successive Projections Algorithm was used to reduce the dimensions of the 261 dimensional hyperspectral datapoints, selecting 33 dimensional hyperspectral datapoints. Principal Component Analysis was used to extract the optimal spectral wavelengths, specifically selecting hyperspectral images at 647.57 nm, 591.78 nm, and 568.36 nm to establish the dataset. (3) Particle Swarm Optimization was used to optimize the Support Vector Machines model, Convolutional Neural Network model, and MobileNet V2 model, which were established to recognize seven types of wheat grains. The comprehensive recognition rates were 93.71%, 95.14%, and 97.71%, respectively. The results indicate that a larger model with more parameters may not necessarily yield better performance. The research shows that the MobileNet V2 network model exhibits superior recognition efficiency, and the integration of hyperspectral image technology with the classification model can accurately identify imperfect wheat grains. [ABSTRACT FROM AUTHOR]

An automatic focusing system is a crucial component of automated microscopes, adjusting the lens-to-object distance to find the optimal focus by maximizing the focus measure (FM) value. This study develops reliable autofocus methods for hyperspectral imaging microscope systems, essential for extracting accurate chemical and spatial information from hyperspectral datacubes. Since FMs are domain- and application-specific, commonly, their performance is evaluated using verified focus positions. For example, in optical microscopy, the sharpness/contrast of visual peculiarities of a sample under testing typically guides as an anchor to determine the best focus position, but this approach is challenging in hyperspectral imaging systems (HSISs), where instant two-dimensional hyperspectral images do not always possess human-comprehensible visual information. To address this, a principal component analysis (PCA) was used to define the optimal ("ideal") optical focus position in HSIS, providing a benchmark for assessing 22 FMs commonly used in other imaging fields. Evaluations utilized hyperspectral images from visible (400–1100 nm) and near-infrared (900–1700 nm) bands across four different HSIS setups with varying magnifications. Results indicate that gradient-based FMs are the fastest and most reliable operators in this context. [ABSTRACT FROM AUTHOR]

Spectral discrimination by animal visions such as human, bird, and butterfly is numerically compared by angular analysis of photo‐response (PR) which will be recorded at photo‐receptors. Hyperspectral imaging system is utilized to simulate various animal vision. Bird vision is acute to discriminate colors among different vegetables due to its evenly spaced and narrow spectral responsivity of photo‐receptors compared to that of human. Butterfly vision is excellent in discriminating red tomato ripening due to the exclusive photo‐receptors detecting only over 600 nm. Even real and fake fruits in the same perceived color for human is discriminated by bird vision. Artificial vision with finely resolved polychromatic artificial cone cells are demonstrated surpassing human vision using visible multispectral camera. This provides insights on designing novel bio‐inspired vision system. [ABSTRACT FROM AUTHOR]

Light fields carry a wealth of information, including intensity, spectrum, and polarization. However, standard cameras capture only the intensity, disregarding other valuable information. While hyperspectral and polarimetric imaging systems capture spectral and polarization information, respectively, in addition to intensity, they are often bulky, slow, and costly. Here, we have developed an encoding metasurface paired with a neural network enabling a normal camera to acquire hyperspectro-polarimetric images from a single snapshot. Our experimental results demonstrate that this metasurface-enhanced camera can accurately resolve full-Stokes polarization across a broad spectral range (700 to 1150 nanometer) from a single snapshot, achieving a spectral sensitivity as high as 0.23 nanometer. In addition, our system captures full-Stokes hyperspectro-polarimetric video in real time at a rate of 28 frames per second, primarily limited by the camera's readout rate. Our encoding metasurface offers a compact, fast, and cost-effective solution for multidimensional imaging that effectively uses information within light fields. [ABSTRACT FROM AUTHOR]

The issues of the same material with different spectra and the same spectra for different materials pose challenges in hyperspectral rock classification. This paper proposes a multidimensional feature network based on 2-D convolutional neural networks (2-D CNNs) and recurrent neural networks (RNNs) for achieving deep combined extraction and fusion of spatial information, such as the rock shape and texture, with spectral information. Experiments are conducted on a hyperspectral rock image dataset obtained by scanning 81 common igneous and metamorphic rock samples using the HySpex hyperspectral sensor imaging system to validate the effectiveness of the proposed network model. The results show that the model achieved an overall classification accuracy of 97.925% and an average classification accuracy of 97.956% on this dataset, surpassing the performances of existing models in the field of rock classification. [ABSTRACT FROM AUTHOR]

Spatial heterodyne Raman spectroscopy has been widely applied in various fields due to its non-contact, nondestructive, fast, high stability, and high spectral resolution characteristics. This article integrated spatial heterodyne Raman spectroscopy with chemometric methodologies to assess the feasibility of peak-to-peak ratio regression, partial least squares regression, support vector machine regression, and non-negative matrix factorization for the quantitative analysis of mixtures. Chemometrics methods were used to model and analyze the interference data in the interferogram domain, and variational mode decomposition was used to extract features from the interference data, further improving the interference data's modeling and prediction accuracy. The results demonstrate that modal intensities obtained through variational mode decomposition of interference data improve the prediction accuracy of regression analysis. Support vector regression exhibited the most favorable predictive performance among the tested models. The root mean square error of cross-validation was reduced from 5.55% to 2.64%, and the prediction root mean square error was reduced from 5.08% to 1.5%. The improved model utilizing interference data showed higher fitting accuracy and more precise sample predictions compared to spectral data modeling. [ABSTRACT FROM AUTHOR]

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Proximate analysis, including ash, volatile matter, moisture, fixed carbon, and calorific value, is a fundamental aspect of fuel testing and serves as the primary method for evaluating coal quality, which is critical for the processing and utilization of coal. The traditional analytical methods involve time-consuming and costly combustion processes, particularly when applied to large volumes of coal that need to be sampled in massive batches. Hyperspectral imaging is promising for the rapid and nondestructive determination of coal quality indices. In this study, a fast and nondestructive coal proximate analysis method with combined spectral-spatial features was developed using a hyperspectral imaging system in the 450–2500 nm range. The processed spectra were evaluated using PLSR, with the most effective MSC spectra selected. To reduce the spectral redundancy and improve the accuracy, the SPA, Boruta, iVISSA, and CARS algorithms were adopted to extract the characteristic wavelengths, and 16 prediction models were constructed and optimized based on the PLSR, RF, BPNN, and LSSVR algorithms within the Optuna framework for each quality indicator. For spatial information, the histogram statistics, gray-level covariance matrix, and Gabor filters were employed to extract the texture features within the characteristic wavelengths. The texture feature-based and combined spectral-texture feature-based prediction models were constructed by applying the spectral modeling strategy, respectively. Compared with the models based on spectral or texture features only, the LSSVR models with combined spectral-texture features achieved the highest prediction accuracy in all quality metrics, with R p 2 values of 0.993, 0.989, 0.979, 0.948, and 0.994 for Ash, VM, MC, FC, and CV, respectively. This study provides a technical reference for hyperspectral imaging technology as a new method for the rapid, nondestructive proximate analysis and quality assessment of coal. [ABSTRACT FROM AUTHOR]

Physical unclonable function (PUF) is attractive in modern encryption technologies. Addressing the disadvantage of slow data storage/authentication in optical PUF is paramount for practical applications but remains an on‐going challenge. Here, a highly efficient PUF strategy based on random structural color domains (SCDs) of cellulose nanocrystal (CNC) is proposed for the first time, combing with hyperspectral imaging system (HIS) for ultrafast storage and authentication. By controlling the growth and fusion behavior of the tactoids of CNC, the SCDs display an irregular and random distribution of colors, shapes, sizes, and reflectance spectra, which grant unique and inherent fingerprint‐like characteristics that are non‐duplicated. Based on images and spectra, these fingerprint features are used to develop two sets of PUF key generation methods, which can be respectively authenticated at the user‐end and the manufacturer‐front‐end that achieving a high coding capacity of at least 22304. Notably, the use of HIS greatly shortens the time of key reading and generation (≈5 s for recording, 0.5–0.7 s for authentication). This new optical PUF labels can not only solve slow data storage and complicated authentication in optical PUF, but also impulse the development of CNC in industrial applications by reducing color uniformity requirement. [ABSTRACT FROM AUTHOR]

Spectral imaging has many applications, from methane detection using satellites to disease detection on crops. However, spectral cameras remain a costly solution ranging from 10 thousand to 100 thousand euros for the hardware alone. Here, we present a low-cost multispectral camera (LC-MSC) with 64 LEDs in eight different colors and a monochrome camera with a hardware cost of 340 euros. Our prototype reproduces spectra accurately when compared to a reference spectrometer to within the spectral width of the LEDs used and the ±1 σ variation over the surface of ceramic reference tiles. The mean absolute difference in reflectance is an overestimate of 0.03 for the LC-MSC as compared to a spectrometer, due to the spectral shape of the tiles. In environmental light levels of 0.5 W m−2 (bright artificial indoor lighting) our approach shows an increase in noise, but still faithfully reproduces discrete reflectance spectra over 400 nm–1000 nm. Our approach is limited in its application by LED bandwidth and availability of specific LED wavelengths. However, unlike with conventional spectral cameras, the pixel pitch of the camera itself is not limited, providing higher image resolution than typical high-end multi- and hyperspectral cameras. For sample conditions where LED illumination bands provide suitable spectral information, our LC-MSC is an interesting low-cost alternative approach to spectral imaging. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging allows to collect both spatial and quasi-continuous spectral information of an object. This work shows the innovative combination of single-pixel microscopy with hyperspectral imaging. An affordable hyperspectral microscope is able to observe micrometer-scale features of inorganic and biological samples and to reconstruct their spectral distribution with a high accuracy (i.e., a spatial and a spectral resolution of 9.0 μm and of 2.1 nm in the visible range, respectively). Furthermore, a statistical algorithm enables the identification of spectral responses of the targeted features as well as their classification. [ABSTRACT FROM AUTHOR]

Hyperspectral images can be widely used in many fields due to their high information richness. However, costly and complex imaging spectrometers limit its growth. Hyperspectral reconstruction aims to obtain the corresponding hyperspectral image from the multispectral image, to reduce the acquisition cost of hyperspectral images. At present, the related works mainly use methods based on deep learning, and they have achieved good results. However, how to fully extract the global spectral and spatial features of hyperspectral images is still not well solved. To address these issues, we propose an Attention and Transformer Complementary Fusion Network (ATCFNet), which is composed of three modules: Multi-angle Input Image Processing (MIIP), Deep Feature Extraction (DFE) and Hyperspectral Reconstruction (HR) modules. Within the DFE module, an improved Transformer module and a novel Multi-scale Spatial Attention (MSA) module are proposed to extract the global spectral relationship and the spatial features of the hyperspectral images, respectively. Moreover, the MIIP module is proposed to extract the features of input multispectral images more effectively and comprehensively. To verify these, we compare the proposed ATCFNet with other excellent reconstruction methods on three hyperspectral image datasets. The results show that our method achieves the best results in these datasets. Code is publicly available at . [ABSTRACT FROM AUTHOR]

This study introduces a novel method utilizing hyperspectral imaging for instantaneous ore-waste analysis of drill cuttings. To implement this technique, we collected samples of drill cuttings at regular depth intervals from five blast holes in an open pit gold mine and subjected them to scanning using a hyperspectral imaging system. Subsequently, we employed two distinct methods for processing the hyperspectral images. A knowledge-based method was used to estimate ore grade within each sampled interval, and a data-driven technique was employed to distinguish the ore and waste for each sample interval. Firstly, leveraging the mixed mineralogical composition of the samples, the Linear Spectral Unmixing (LSU) technique was utilized to predict ore grade for each sample. Additionally, the Gradient Boosting Classifier (GBC) was used as an efficient data-driven approach to classify ore-waste samples. Both methods rendered accurate results when they were compared with results obtained through laboratory X-ray diffraction (XRD) analysis and gold assay analysis for the same sample intervals. Adopting the proposed methodology in open pit mine operations can significantly enhance the process of grade control during blast hole drilling. This includes reducing costs, saving time, minimizing uncertainty in ore grade estimation, and establishing more precise ore-waste boundaries in resource block models. [ABSTRACT FROM AUTHOR]

The study explores the potential of next-generation satellite hyperspectral imaging systems for screening and predicting surface-soil contamination and degradation by exploiting various spectral indices and signature-matching techniques at a heavily industrialized area in India. The soil moisture content, desertification and salinity status, clay or fine material content, heavy metal content, vegetation health status, and stress levels were assessed from continuum-removed spectral reflectance values. Results indicated the presence of water in two tailings ponds, high salinity, and desertification values in most of the tailings ponds and dump sites, clay boundary liner along four ponds, high heavy metal indices along three ponds and all dump sites, highly stressed vegetation near all tailings ponds and coal dump sites, and pollutants in nearby water channels. The results suggest a strategy for the initial identification of priority areas for ground-based investigations and an alternative rapid methodology to monitor large industrial hubs in India. [ABSTRACT FROM AUTHOR]

In contemporary agriculture, enhancing the efficient production of crops and optimizing resource utilization have become paramount objectives. Garlic growth and quality are influenced by various factors, with fertilizers playing a pivotal role in shaping both aspects. This study aimed to develop classification models for distinguishing garlic fertilizer application differences by employing statistical and machine learning techniques, such as partial least squares (PLS), based on data acquired from a ground-based hyperspectral imaging system in the agricultural sector. The garlic variety chosen for this study was Hongsan, and the fertilizer application plots were segmented into three distinct sections. Data were acquired within the VIS/NIR wavelength range using hyperspectral imaging. Following data acquisition, the standard normal variate (SNV) pre-processing technique was applied to enhance the dataset. To identify the optimal wavelengths, various techniques such as sequential forward selection (SFS), successive projections algorithm (SPA), variable importance in projection (VIP), and interval partial least squares (iPLS) were employed, resulting in the selection of 12 optimal wavelengths. For the fertilizer application difference model, six integrated vegetation indices were chosen for comparison with existing growth indicators. Using the same methodology, the model construction showed accuracies of 90.7% for PLS. Thus, the proposed model suggests that efficient regulation of garlic fertilizer application can be achieved by utilizing statistical and machine learning techniques. [ABSTRACT FROM AUTHOR]