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Solar-induced fluorescence (SIF) is a proxy of ecosystem photosynthesis that often scales linearly with gross primary productivity (GPP) at the canopy scale. However, the mechanistic relationship between GPP and SIF is still uncertain, especially at smaller temporal and spatial scales. We deployed a ultra-hyperspectral imager over two grassland sites in California throughout a soil moisture dry down. The imager has high spatial resolution that limits mixed pixels, enabling differentiation between plants and leaves within one scene. We find that imager SIF correlates well with diurnal changes in leaf-level physiology and gross primary productivity under well-watered conditions. These relationships deteriorate throughout the dry down event. Our results demonstrate an advancement in SIF imaging with new possibilities in remotely sensing plant canopies from the leaf to the ecosystem. These data can be used to resolve outstanding questions regarding SIF's meaning and usefulness in terrestrial ecosystem monitoring.

BACKGROUND RESULTS CONCLUSION Water content and chlorophyll content are important indicators for monitoring rice growth status. Simultaneous detection of water content and chlorophyll content is of significance. Different varieties of rice show differences in phenotype, resulting in the difficulties of establishing a universal model. In this study, hyperspectral imaging was used to detect the Soil and Plant Analyzer Development (SPAD) values and water content of fresh rice leaves of three rice varieties (Jiahua 1, Xiushui 121 and Xiushui 134).Both partial least squares regression and convolutional neural networks were used to establish single‐task and multi‐task models. Transfer component analysis (TCA) was used as transfer learning to learn the common features to achieve an approximate identical distribution between any two varieties. Single‐task and multi‐task models were also built using the features of the source domain, and these models were applied to the target domain. These results indicated that for models of each rice variety the prediction accuracy of most multi‐task models was close to that of single‐task models. As for TCA, the results showed that the single‐task model achieved good performance for all transfer learning tasks.Compared with the original model, good and differentiated results were obtained for the models using features learned by TCA for both the source domain and target domain. The multi‐task models could be constructed to predict SPAD values and water content simultaneously and then transferred to another rice variety, which could improve the efficiency of model construction and realize rapid detection of rice growth indicators. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

• The spectral characteristics of different types of deterioration are analyzed. • The normalized spectral index is constructed to preliminarily identify the deterioration. • The relationship between spectral reflectance and Schmidt rebound value is established. • The deterioration identification models are established based on hyperspectral image texture features. Deterioration investigation is an essential foundation for understanding the preservation status of stone cultural heritage, as well as for carrying out emergency and preventive conservation. Traditional photogrammetry method for deterioration investigation in stone cultural heritage heavily relies on personnel experience and has low automation. To accurately evaluate the degree of deterioration and quantify its scale, different algorithms are used to establish the rebound value prediction model and deterioration identification model based on the hyperspectral image. The effects of different wavelength selection methods and different classification models are compared. The results show that the rebound value inversion model constructed by CARS and PLS delivers the most accurate forecasts, with R2 being no less than 0.85. The maximum error of the model when applied in the field does not exceed 20%. Different types of deterioration can be initially identified by the normalized spectral index constructed from the 530 nm and 675 nm wavelengths. In addition, all four classification models based on hyperspectral imaging texture features can identify different types of deterioration. The LGBM model has the highest identification accuracy of 0.98. It also has good performance in field identification. This study provides a new method for deterioration investigation in stone cultural heritage. [ABSTRACT FROM AUTHOR]

Full‐field transmission X‐ray microscopy has been recently implemented at the hard X‐ray ROCK–SOLEIL quick‐EXAFS beamline, adding micrometre spatial resolution to the second time resolution characterizing the beamline. Benefiting from a beam size versatility due to the beamline focusing optics, full‐field hyperspectral XANES imaging has been successfully used at the Fe K‐edge for monitoring the pressure‐induced spin transition of a 150 µm × 150 µm Fe(o‐phen)2(NCS)2 single crystal and the charge of millimetre‐sized LiFePO4 battery electrodes. Hyperspectral imaging over 2000 eV has been reported for the simultaneous monitoring of Fe and Cu speciation changes during activation of a FeCu bimetallic catalyst along a millimetre‐sized catalyst bed. Strategies of data acquisition and post‐data analysis using Jupyter notebooks and multivariate data analysis are presented, and the gain obtained using full‐field hyperspectral quick‐EXAFS imaging for studies of functional materials under process conditions in comparison with macroscopic information obtained by non‐spatially resolved quick‐EXAFS techniques is discussed. [ABSTRACT FROM AUTHOR]

Changing the thermo -mechanical properties, variety of formulation and storage conditions, 36 samples of low -fat mozzarella cheese were produced and their hardness, adhesiveness, cohesiveness, springiness, cohesiveness, gumminess and chewiness were evaluated by TPA followed by analyzing data using completely randomized factorial design with univariate analysis through IBM SPSS Statistics. 26. Then, Imaging of the same samples with a Hyperspectral camera in the range of 400 -1000 nm as well as pre -processing the spectra and preferring the important wavelengths by feature selection algorithms to developed the calibration models including multiple linear regression algorithms, partial least squares regression, support vector machine with a linear kernel, multilayer perceptron neural network, random forests and majority voting algorithm was performed in Python software followed by the performance of models were evaluated. Results showed that the more increased the stretching time in hot water from 2 to 8 minutes, the more the hardness, springiness, gumminess and chewiness and cohesiveness increased, but adhesiveness was decreased. The majority vote algorithm (VOTING) revealed the highest performance in hardness prediction (R 2p=0.878, RMSEp=2606.52 and RPD=2.12) and was able to predict the cohesiveness of mozzarella with higher accuracy more than other algorithms. Multiple linear regression couldn't predict the adhesiveness properly, but random forest method with high performance predicted this feature (R 2p=0.808, RMSE=56.49, RPD=1.90). The multi -layer perceptron neural network with the least error, predicted springiness (R 2p = 0.848, RMSEp = 0.094, RPD = 2.12) and chewiness (R 2p = 0.84, RMSEp = 1117.21, RPD = 1.96) with high accuracy. All methods except random forest were able to predict the gumminess of mozzarella with high efficiency. In this study, it was cleared that the process conditions had significant effects on the textural characteristics and the Hyperspectral imaging was found to be a suitable alternative method for estimating the textural characteristics of mozzarella cheese. [ABSTRACT FROM AUTHOR]

Summary: During harvesting, transportation and storage of kiwifruit, the flesh is often bruised by collision or compression. However, the bruises in kiwifruit are extremely difficult to recognise by naked eyes and are called hidden bruises. Accordingly, a fast method for detecting hidden bruises in kiwifruit was developed in this study based on hyperspectral imaging (HSI) coupled with deep learning. The spectral range (924–1277 nm) and feature wavelengths (928.19, 1051.03 and 1190.47 nm) sensitive to hidden bruises in kiwifruit were selected using the principal component analysis (PCA). Subsequently, three‐channel images (Dataset 1), grayscale images (Dataset 2) and pseudo‐colour images (Dataset 3) were generated according to the images of feature wavelengths of the kiwifruit. The YOLOv5s model for detecting the hidden bruised areas of the kiwifruit was developed using these three datasets. The results showed that the YOLOv5s detection model performed best at Dataset 1, and the values of Precision, Recall, F1, mAP and FNR of this model were 98.25%, 97.50%, 97.87%, 99.12% and 2.50% respectively. The study showed that HSI technology combined with the YOLOv5s model can effectively detect hidden bruises in kiwifruit, providing references for detecting hidden bruises in other fruit. [ABSTRACT FROM AUTHOR]

Coffee quality and varieties matter for its ultimate flavor. As multiple factors influence taste during coffee production, hyperspectral imaging (HSI) stands out with great potential for quality assessment, defect supervision, and character identification. With a relatively comprehensive summary of current HSI-based research in coffee, this literature review critically reveals HSI's competence and present shortcomings in practical applications within the coffee industry. Sequentially, potential applications of HSI technology in coffee component analysis and flavor estimation, blemish diagnosis, and origins or attributes identification are illustrated. Finally, several key challenges and future perspectives of HSI are indicated from a practical and sustainable perspective, with digitalization as a major goal in the coffee industry. [ABSTRACT FROM AUTHOR]

Soluble solid content (SSC) and firmness are significant indexes to evaluate the quality of wolfberry. This study employed hyperspectral imaging (HSI) technology for the rapid detection and visualization of the distribution of SSC and firmness in mature wolfberries. The hyperspectral images of Ningqi 1 and Ningqi 7 were collected in the range of 400–1000 nm. The image segmentation method was used to determine the region of interest (ROI) of the wolfberry samples and extract the mean spectra, and the performance of the four preprocessing techniques was evaluated based on the partial least squares (PLSR) model, which concluded that the standard normal variable transformation (SNV) and multiple scattering correction (MSC) preprocessing methods were able to achieve the optimal results. Principal component analysis (PCA), successive projection algorithm (SPA), competitive adaptive reweighted sampling method (CARS) and their combination were used to select the characteristic wavelength, with CARS-SPA being more accurate. PLSR, support vector machine regression (SVR) and backpropagation genetic algorithm (BPNN-GA) models were used to predict the soluble solid content and firmness of wolfberry by full wavelength and characteristic wavelength, respectively. The optimal model for SSC and firmness of Ningqi 1 was identified as MSC-CARS-SPA-BPNN-GA, with Rp2 of 0.949 and 0.913, RMSEP of 0.365 and 0.524, and RPD of 4.104 and 3.422, respectively. For Ningqi 7, the optimal model was SNV-CARS-SPA-BPNN-GA, with Rp2 of 0.936 and 0.880, RMSEP of 0.364 and 0.537, and RPD of 3.860 and 2.706, respectively. Finally, these optimal models were utilized to visualize the distribution of SSC and firmness in the ROI. The findings underscore the rapid and precise nature of hyperspectral imaging in detecting the SSC and firmness of wolfberry, thereby establishing a technological and theoretical foundation for expedited wolfberry quality assessment. [ABSTRACT FROM AUTHOR]

The mechanical damage to the Aronia melanocarpa caused by collisions during the mechanical harvesting process can reduce its storage time and market value. The study employs a pendulum impact test to analyze the impact behavior between A. melanocarpa fruits and impact surfaces. The impact surface material is steel plate, acrylic board, corrugated cardboard and pearl cotton plate. The tests were performed on a pendulum test platform, using a high-speed photography system to record the absorbed energy and contact area. The hyperspectral imaging technology is employed to detect fruits damage, and then the Support Vector Machine supervised learning algorithm was applied to classify its damaged regions. The degree of damage for A. melanocarpa is classified. The results indicate that the damage rate increases from 34.92% to 52.52%, and the absorbed energy increases from 2.1185 to 7.9009 mJ, with the pendulum height increases from 200 to 650mm, when the impact surface material is steel plate and the moisture content of the samples was 82%. With the increase of absorbed energy and damage rate, the results show that pendulum height, impact face material, and fruit moisture content have effects on the spectral reflectance of the samples. The impact surface material has the greatest influence on the damage rate, followed by the pendulum height, and the smallest is the water content. Furthermore, the results reveal that a strong linear correlation between contact area and damage rate, as well as absorption energy. The study provides a theoretical basis for the identification and classification of A. melanocarpa damage, as well as the optimization of design parameters for its mechanical harvesting. [ABSTRACT FROM AUTHOR]

A rapid and non-destructive machine learning approach was developed to identify the geographical origin and predict nutrient contents of dried wolfberries by analyzing the hyperspectral images. 1826 images of dried wolfberry samples from four different geographical origins in China were collected and used as the database for this study. The raw spectral data was pre-processed and the characteristic wavelengths between 370 and 1043 nm was extracted to establish the datasets applicable for the analysis. The novel lightweight neural network model was developed based on group convolution and average pooling. By comparing the new approach with the other two existing neural network methods, it shows that the new model performs as well as or better than the existing algorithms. This study demonstrates the feasibility of identifying the geographic origin classification and nutrient prediction based on hyperspectral data of dried wolfberries. The non-invasive method provides a reference for future research of other similar products. [ABSTRACT FROM AUTHOR]

After oil spills occur in the ocean, oil pollutants usually appear in the form of oil emulsions under the influence of hydrodynamics. Hyperspectral remote sensing technology, which provides abundant spectral information of ground objects, has the potential of fine-grained classification on the types of oil emulsions. Aiming at the practical applications of oil emulsion extraction in hyperspectral images (HSIs), this study proposes a semi-supervised model for oil emulsion identification by integrating an image segmentation algorithm with a deep-learning-based classification model. In the proposed approach, the training data were filtered from HSI using an image segmentation algorithm, based on which a 1-dimensional convolutional neural network (1D-CNN) was trained to identify oil emulsions in the HSI. The model was tested on the HSIs of Deepwater Horizon oil spills obtained by AVIRIS. The overall accuracy and standard performance measurements of the proposed model are higher than 94% on the extracted dataset. The results indicated that the proposed model achieved similar detection results on sea water as the supervised model, and even higher accuracies on oil emulsion type identification. As a semi-supervised model, it also avoids the lengthy and time-consuming data labelling and has the potential for operational oil emulsions extraction and quantification. [ABSTRACT FROM AUTHOR]

Soluble solids content (SSC) is an essential internal quality attribute of fruits that directly affects consumers' degree of satisfaction. The traditional determination method, the refractometer, is characterized by inherent drawbacks of destructiveness, labor intensiveness, and low efficiency. Visible/near-infrared (VIS/NIR) spectroscopy and hyperspectral imaging (HSI) can be promising alternative approaches as being non-destructive, accurate, and rapid and have attracted extensive attention. This review endeavors to elucidate the advantages and limitations of applying VIS/NIR and HSI techniques in determining fruit SSC, drawing upon a comprehensive analysis of the pertinent literature. The latest progress of instrument configuration is described, and an outline for crucial steps involved in data acquisition and analysis is presented to provide empirical support for future research. Notably, the main internal and external factors that interfere with the model performance in complex application scenarios are comprehensively discussed for the first time. Additionally, the advances in strategies devised to compensate for these interference are summarized. To facilitate the transition of VIS/NIR and HSI techniques for fruit SSC into practical use, future research activities should be focused on addressing the challenges in big data acquisition, sample representativeness, feature fusion, model verification and interpretation, and model improvement. [ABSTRACT FROM AUTHOR]

Background: Seed vigor identification is critical to guaranteeing the quality and yield of maize. Although seeds with impaired vigor may germinate under normal conditions, planting under unfavorable conditions makes it difficult to produce healthy plants. Therefore, non-destructive and rapid detection of seed vigor using hyperspectral imaging (HSI) technology is crucial for improving crop production efficiency. Methods: Hyperspectral images of maize seeds were acquired employing the HSI system, the original spectra were preprocessed using Savitzky–Golay smoothing and multiplicative scatter correction, and the feature wavelengths were extracted using the successive projections algorithm (SPA). Discriminant models were constructed based on support vector machine (SVM), random forest, artificial neural network (ANN), and convolutional neural network (CNN-DC). Results: The results showed that SVM, ANN, and CNN-DC could discriminate well between maize seeds with different vigor levels, and their accuracy rate was over 70%. The SPA algorithm showed that the RMSE value achieved a minimum of 0.3406, while the number of variables was 49. The CNN-DC model outperformed the other models, which reached the highest accuracy of 92.06%. This study demonstrates that DL combined with HSI has excellent potential for identifying seed vigor. Conclusions: This study shows that the proposed method has excellent results for hyperspectral image data processing and can accurately identify maize seed vigor. [ABSTRACT FROM AUTHOR]

Assessing ecosystem health on a large scale is crucial for a wide range of management and regulatory decisions. Technologies such as hyperspectral imaging allow noninvasive and rapid estimation of key attributes based on observed reflectance. However, these images are high-dimensional and real-world applications require models based on fewer wavelengths. This paper proposes a new wavelength selection and feature extraction method for hyperspectral image analysis based on genetic programming to automatically select key wavelength regions and informative image features. A dataset of hyperspectral images of sediment in the field was collected and paired with ground-truth measurements of the sediment porosity and organic matter content. Two new program structures were proposed to construct feature extraction trees from either the mean reflectance spectra (spectra-based) or full hyperspectral images (image-based). SVR models were constructed to predict attributes based on the extracted features. Various regression models were used to predict the porosity and organic matter content. Full-wavelength models were constructed to reliably predict the organic matter content. The proposed spectra-based genetic programming solutions show competitive results compared to common wavelength selection methods, such as SPA, CARS, and RC. Finally, the best-evolved solution was applied to predict sediment organic matter content across all collected images. [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]

Background: As an extension of the central nervous system (CNS), the retina shares many similarities with the brain and can manifest signs of various neurological diseases, including Alzheimer's disease (AD). Objective: To investigate the retinal spectral features and develop a classification model to differentiate individuals with different brain amyloid levels. Methods: Sixty-six participants with varying brain amyloid-β protein levels were non-invasively imaged using a hyperspectral retinal camera in the wavelength range of 450–900 nm in 5 nm steps. Multiple retina features from the central and superior views were selected and analyzed to identify their variability among individuals with different brain amyloid loads. Results: The retinal reflectance spectra in the 450–585 nm wavelengths exhibited a significant difference in individuals with increasing brain amyloid. The retinal features in the superior view showed higher inter-subject variability. A classification model was trained to differentiate individuals with varying amyloid levels using the spectra of extracted retinal features. The performance of the spectral classification model was dependent upon retinal features and showed 0.758–0.879 accuracy, 0.718–0.909 sensitivity, 0.764–0.912 specificity, and 0.745–0.891 area under curve for the right eye. Conclusions: This study highlights the spectral variation of retinal features associated with brain amyloid loads. It also demonstrates the feasibility of the retinal hyperspectral imaging technique as a potential method to identify individuals in the preclinical phase of AD as an inexpensive alternative to brain imaging. [ABSTRACT FROM AUTHOR]

This study introduces a novel method for detecting and measuring transparent glass sheets using hyperspectral imaging (HSI). The main goal of this study is to create a conversion technique that can accurately display spectral information from collected images, particularly in the visible light spectrum (VIS) and near-infrared (NIR) areas. This technique enables the capture of relevant spectral data when used with images provided by industrial cameras. The next step in this investigation is using principal component analysis to examine the obtained hyperspectral images derived from different treated glass samples. This analytical procedure standardizes the magnitude of light wavelengths that are inherent in the HSI images. The simulated spectral profiles are obtained using the generalized inverse matrix technique on the normalized HSI images. These profiles are then matched with spectroscopic data obtained from microscopic imaging, resulting in the observation of distinct dispersion patterns. The novel use of images coloring methods effectively displays the thickness of the glass processing sheet in a visually noticeable way. Based on empirical research, changes in the thickness of the glass coating in the NIR-HSI range cause significant changes in the transmission of infrared light at different wavelengths within the NIR spectrum. This phenomenon serves as the foundation for the study of film thickness. The root mean square error inside the NIR area is impressively low, calculated to be just 0.02. This highlights the high level of accuracy achieved by the technique stated above. Potential areas of investigation that arise from this study are incorporating the proposed approach into the design of a real-time, wide-scale automated optical inspection system. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging has shown great promise for diagnostic applications, particularly in cancer surgery. However, non-bulk tissue-related spectral variations complicate the data analysis. Common techniques, such as standard normal variate normalization, often lead to a loss of amplitude and scattering information. This study investigates a novel approach to address these spectral variations in hyperspectral images of optical phantoms and excised human breast tissue. Our method separates surface and volume reflectance, hypothesizing that spectral variability arises from significant variations in surface reflectance across pixels. An illumination setup was developed to measure samples with a hyperspectral camera from different axial positions but with identical zenith angles. This configuration, combined with a novel data analysis approach, allows for the estimation and separation of surface reflectance for each direction and volume reflectance across all directions. Validated with optical phantoms, our method achieved an 83% reduction in spectral variability. Its functionality was further demonstrated in excised human breast tissue. Our method effectively addresses variations caused by surface reflectance or glare while conserving surface reflectance information, which may enhance sample analysis and evaluation. It benefits samples with unknown refractive index spectra and can be easily adapted and applied across a wide range of fields where hyperspectral imaging is used. [ABSTRACT FROM AUTHOR]

Rapid detection of plant phenotypic traits is crucial for plant breeding and cultivation. Traditional measurement methods are carried out by richexperienced agronomists, which are time-consuming and labor-intensive. However, with the increasing demand for rapid and high-throughput testing in tea plants traits, digital breeding and smart cultivation of tea plants rely heavily on precise plant phenotypic trait measurement techniques, among which hyperspectral imaging (HSI) technology stands out for its ability to provide real-time and rich-information. In this paper, we provide a comprehensive overview of the principles of hyperspectral imaging technology, the processing methods of cubic data, and relevant algorithms in tea plant phenomics, reviewing the progress of applying hyperspectral imaging technology to obtain information on tea plant phenotypes, growth conditions, and quality indicators under environmental stress. Lastly, we discuss the challenges faced by HSI technology in the detection of tea plant phenotypic traits from different perspectives, propose possible solutions, and envision the potential development prospects of HSI technology in the digital breeding and smart cultivation of tea plants. This review aims to provide theoretical and technical support for the application of HSI technology in detecting tea plant phenotypic information, further promoting the trend of developing high quality and high yield tea leaves. [ABSTRACT FROM AUTHOR]

AbstractEarly 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]

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]

Fluorescence photography was used to evaluate the effects of bentazon, an herbicide that inhibits electron transport in photosystem II, on sunflower, taken as a model plant. We measured chlorophyll fluorescence to visualize the penetration, distribution, and effect of the herbicide on the plant. Our results showed that bentazon penetrates the leaf within 10–16 min, reaching full depth within 40 min. Also, we show that the herbicide is distributed along the conducting tissues of the leaf. The presence of necrotic spots, as well as the size of increased-fluorescence areas, were positively correlated to the concentration of herbicide. These findings suggest that fluorescence imaging may be a useful tool for observing the absorption and distribution of photosystem II-inhibiting herbicides in plants as an alternative to radioactive labeling in some cases. [ABSTRACT FROM AUTHOR]

High expectations are placed on microalgae as a sustainable source of valuable biomolecules. Robust methods to control microalgae cultivation processes are needed to enhance their efficiency and, thereafter, increase the profitability of microalgae-based products. To meet this need, a non-invasive monitoring method based on a hyperspectral imager was developed for laboratory scale and afterwards tested on industrial scale cultivations. In the laboratory experiments, reference data for microalgal biomass concentration was gathered to construct 1) a vegetation index-based linear regression model and 2) a one-dimensional convolutional neural network model to resolve microalgae biomass concentration from the spectral images. The two modelling approaches were compared. The mean absolute percentage error (MAPE) for the index-based model was 15–24%, with the standard deviation (SD) of 13-18 for the different species. MAPE for the convolutional neural network was 11–26% (SD = 10–22). Both models predicted the biomass well. The convolutional neural network could also classify the monocultures of green algae by species (accuracy of 97–99%). The index-based model was fast to construct and easy to interpret. The index-based monitoring was also tested in an industrial setup demonstrating a promising ability to retrieve microalgae-biomass-based signals in different cultivation systems. [ABSTRACT FROM AUTHOR]

Biofluorescence in certain seaweed species has been studied in association with their respective chlorophyll type and concentration. However, there is a paucity of information on the biofluorescent signature of macroalgal species at different life stages. Here we document that hyperspectral imaging differentiates between the fluorescent signatures of 5 macroalgal species (Ascophyllum nodosum, Fucus vesiculosus, Laminaria digitata, Palmaria palmata, and Saccharina latissima) collected from Tromsø, northern Norway (69-70 °N). Non-reproductive (NR) blades were collected for all species and reproductively mature blades (R) with sori were collected for three species. Hyperspectral imaging of collected blades was conducted under excitation lighting (~445 nm). We further assessed whether biofluorescence is reflected in the reproductive phenology of three selected species, L. digitata, P. palmata, and S. latissima. The spectral radiance of fluorescence emissions was found to be consistently different between R and NR blades. The greatest difference between R and NR blades was between the rhodophyte P. palmata (0.0075 W nm-1sr-1m-2 at 725 nm) followed by the phaeophytes L. digitata (0.03 W nm-1sr-1m-2 at 730-740 nm) and S. latissima (0.00225 W nm-1sr-1m-2 at 735-745 nm). Biofluorescence monitoring with hyperspectral imaging should be considered for further applicability in commercial seaweed operations. [ABSTRACT FROM AUTHOR]

This paper describes the design, implementation, and usage of a Python package called Hyperspectral Python (HypPy). Proprietary software for processing hyperspectral images is expensive, and tools developed using these packages cannot be freely distributed. The idea of HypPy is to be able to process hyperspectral images using free and open-source software. HypPy was developed using Python and relies on the array-processing capabilities of packages like NumPy and SciPy. HypPy was designed with practical imaging spectrometry in mind and has implemented a number of novel ideas. To name a few of these ideas, HypPy has BandMath and SpectralMath tools for processing images and spectra using Python statements, can process spectral libraries as if they were images, and can address bands by wavelength rather than band number. We expect HypPy to be beneficial for research, education, and projects using hyperspectral data because it is flexible and versatile. [ABSTRACT FROM AUTHOR]

Color, firmness, soluble solid content, and pH are important indices for assessing the quality and maturity of loquats. To explore the feasibility of rapid and non‐destructive determination of loquat quality and maturity, this study utilized hyperspectral imaging combined with chemometrics to predict four quality indices of loquats and discriminate their maturity. Partial least squares regression models were developed using both raw and pre‐processed spectral data to determine the optimal pre‐processing method of multiple scattering correction and standard normal variate (SNV). The competitive adaptive reweighted sampling (CARS) and successive projection algorithms were used to extract spectral features. Feature wavelength models were subsequently developed using multiple linear regression (MLR) and error back propagation neural network. Finally, maturity determination models for loquats were developed by partial least squares discrimination analysis (PLS‐DA), support vector machine, and random forest. The SNV‐CARS‐MLR model performed relatively better than the other models for predicting four quality indices. The PLS‐DA model exhibited superior performance, with discrimination accuracies of 99.19% and 96.67% for the calibration and prediction sets. This study demonstrates that integrating hyperspectral imaging and chemometrics enables rapid and non‐destructive determination of loquat quality and maturity. [ABSTRACT FROM AUTHOR]

Skin cancer is the predominant form of cancer worldwide, including 75% of all cancer cases. This study aims to evaluate the effectiveness of the spectrum-aided visual enhancer (SAVE) in detecting skin cancer. This paper presents the development of a novel algorithm for snapshot hyperspectral conversion, capable of converting RGB images into hyperspectral images (HSI). The integration of band selection with HSI has facilitated the identification of a set of narrow band images (NBI) from the RGB images. This study utilizes various iterations of the You Only Look Once (YOLO) machine learning (ML) framework to assess the precision, recall, and mean average precision in the detection of skin cancer. YOLO is commonly preferred in medical diagnostics due to its real-time processing speed and accuracy, which are essential for delivering effective and efficient patient care. The precision, recall, and mean average precision (mAP) of the SAVE images show a notable enhancement in comparison to the RGB images. This work has the potential to greatly enhance the efficiency of skin cancer detection, as well as improve early detection rates and diagnostic accuracy. Consequently, it may lead to a reduction in both morbidity and mortality rates. [ABSTRACT FROM AUTHOR]

The gold standard for skin cancer diagnosis is surgical excisional biopsy and histopathological examination. Several non-invasive diagnostic techniques exist, although they have not yet translated into clinical use. This is a proof-of-concept study to assess the possibility of imaging an angiosarcoma in the periocular area. We use laser speckle, hyperspectral, and photoacoustic imaging to monitor blood perfusion and oxygen saturation, as well as the molecular composition of the tissue. The information obtained from each imaging modality was combined in order to yield a more comprehensive picture of the function, as well as molecular composition of a rapidly growing cutaneous angiosarcoma in the periocular area. We found an increase in perfusion coupled with a reduction in oxygen saturation in the angiosarcoma. We could also extract the molecular composition of the angiosarcoma at a depth, depicting both the oxygen saturation and highlighting the presence of connective tissue via collagen. We demonstrate the different physiological parameters that can be obtained with the different techniques and how these can be combined to provide detailed 3D maps of the functional and molecular properties of tumors useful in preoperative assessment. [ABSTRACT FROM AUTHOR]

Thermally processed meat may contain harmful compounds, including polycyclic aromatic hydrocarbons (PAHs). This study constructed, for the first time, the comprehensive PAH index (CPI) concentration (phenanthrene [26.47%], acenaphthene [21.83%], pyrene [18.64%], fluoranthene [17.11%], fluorene [8.49%], and anthracene [7.46%]). A visible near‐infrared (Vis–NIR) hyperspectral image (HSI) system was employed to detect CPI in 150 roasted Tan lamb samples. Furthermore, two‐dimensional correlation spectra were used to identify spectral features and reveal the order of chemical bond changes under the characteristic peaks at 579–737–631–449 nm. The results indicated that competitive adaptive reweighted sampling–multiple linear regression quantitative prediction model worked the best with calibration set coefficient of determination of 0.9161, calibration set coefficient of root mean square error of 2.3426 µg/kg, R‐squared prediction of 0.8469, and root mean square error of prediction of 2.4119 µg/kg. Finally, PAH content distributions were visualized using the best prediction model. This study aimed to propose a feasible method for CPI in roasted Tan lamb detection based on Vis–NIR HSI coupled with multivariate analysis methods. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging is a powerful analytical technique that is drawing more attention among the different disciplines due to its feasibility for in situ applications on imaging of living organisms. In this review article, we summarize the development and implementation of this technique in the different fields of science. It is important to acknowledge that the hyperspectral microscope, a specialized imaging tool, combines the power of microscopy with the ability to capture detailed spectral information about the sample studied. Unlike traditional microscopes, which only capture spatial information about a sample, hyperspectral microscopes also capture spectral data from each spatial point, resulting in a 3D data cube. The latest applications have been studied and described, including examples from the environmental and medical field. The broad possibilities related to hyperspectral microscopy were discussed and highlighted showing successful results. Hyperspectral microscopy is a revolutionary and reliable technique in the world of characterizing analytical tools. [ABSTRACT FROM AUTHOR]

Wide-field mid-infrared (MIR) hyperspectral imaging offers a promising approach for studying heterogeneous chemical systems due to its ability to independently characterize the molecular properties of different regions of a sample. However, applications of wide-field MIR microscopy are limited to spatial resolutions no better than ∼1 μm. While methods exist to overcome the classical diffraction limit of ∼λ/2, chromatic aberration from transmissive imaging reduces the achievable resolution. Here we describe the design and implementation of a simple MIR achromatic lens combination that we believe will aid in the development of resolution-enhanced wide-field MIR hyperspectral optical and chemical absorption imaging. We also examine the use of this doublet lens to image through polystyrene microspheres, an emerging and simple means for enhancing spatial resolution. [ABSTRACT FROM AUTHOR]

Acne Vulgaris is a common type of skin disease that affects more than 85% of teenagers and frequently continues even in adulthood. While it is not a dangerous skin disease, it can significantly impact the quality of life. Hyperspectral imaging (HSI), which captures a wide spectrum of light, has emerged as a tool for the detection and diagnosis of various skin conditions. However, due to the high cost of specialised HS cameras, it is limited in its use in clinical settings. In this research, a novel acne detection system that will utilise reconstructed hyperspectral (HS) images from RGB images is proposed. A dataset of reconstructed HS images is created using the best-performing HS reconstruction model from our previous research. A new acne detection algorithm that is based on reconstructed HS images and RetinaNet algorithm is introduced. The results indicate that the proposed algorithm surpasses other techniques based on RGB images. Additionally, reconstructed HS images offer a promising and cost-effective alternative to using expensive HSI equipment for detecting conditions like acne or other medical issues. [ABSTRACT FROM AUTHOR]

Detection of kiwifruit bruising time is one of the essential indicators for reducing postharvest losses and assessing internal quality. To investigate if the bruised time of kiwifruit could be non-destructively detected, hyperspectral imaging (HSI) technology was used to acquire hyperspectral images of kiwifruit from two different varieties at various bruising time. 70 kiwifruit samples from each of the two varieties were included in the study, with a total of 490 (7 × 70) hyperspectral images collected for each variety across seven bruising time. The spectral feature of the bruised areas of kiwifruit were extracted using the uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA) methods, combined with the successive projection algorithm (SPA), respectively. Besides, the textural feature of the bruised kiwifruit were extracted using the gray-level co-occurrence matrix (GLCM). Finally, Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and one-dimensional convolutional neural network (1D-CNN) were built to identify the bruising time of kiwifruit using the spectral feature, textural feature, and fused data (spectra and texture), respectively. The results suggests that the 1D-CNN model built by fused data was the most effective in identifying kiwifruit bruising time, with identification accuracies of 94.55% for 'Hayward', 97.95% for 'Wanhong', and 95.23% for the mixed kiwifruits. The studies suggests that the HSI technique combined with machine learning could effectively identify the bruising time of kiwifruit and provide a reference for kiwifruit quality grading detection. [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]

The necessity for precise determination of fruit storage durations is paramount in both industrial and domestic spheres, with hyperspectral imaging (HSI) technology emerging as a pivotal tool for prognosticating the physicochemical attributes indicative of mature fruit quality. This investigation employed hyperspectral imaging to conduct a noninvasive analysis of variations in soluble solids content (SSC), hardness, and moisture content (MC) in jujube fruit over the course of storage at divergent temperatures. Throughout the storage intervals (0, 7, 14, and 21 days) at varying temperatures (4 and 20°C), both physicochemical and spectral data were amassed. The raw spectral information underwent preprocessing through multiple scattering correction (MSC), standard normal variation (SNV), and Savitzky–Golay (SG) algorithms to refine the methodologies for SSC, hardness, and moisture content. Subsequently, the competitive adaptive reweighted sampling (CARS) algorithm facilitated the discernment and elimination of extraneous variables, thereby enhancing feature wavelength extraction. This process underpinned the development of partial least squares regression (PLSR), back propagation (BP), and genetic algorithm‐back propagation (GA‐BP) models predicated on CARS‐derived features, culminating in the selection of an optimal model. The findings underscore the capability of hyperspectral imaging technology to swiftly and nondestructively ascertain the SSC, hardness, and MC of jujube throughout the storage phase, thereby enabling the assessment of quality attributes over varying storage durations and facilitating the surveillance of jujube quality maintenance during storage. Practical Applications: The winter jujube garners appreciation from consumers owing to its exquisite flavor, yet its quality diminishes over time in storage due to various factors, thereby impacting its market value. Consequently, it is imperative to surveil the quality alterations of jujube throughout its storage to mitigate the degradation of its quality. Hyperspectral imaging technology offers a sophisticated means to forecast the physicochemical index changes indicative of the fruit's quality at maturity. This research delineates the development of predictive models for the soluble solids, hardness, and moisture content of jujube at divergent temperatures throughout the storage interval, selecting the paramount model through a holistic assessment, thereby fully harnessing the capabilities of hyperspectral imaging technology in monitoring jujube quality during storage. Moreover, the methodology employed herein is adaptable to other fruits in storage, harboring the potential for future application in the real‐time quality monitoring of fruits as they exit the storage facilities. [ABSTRACT FROM AUTHOR]

Accurate intraoperative assessment of organ perfusion is a pivotal determinant in preserving organ function e.g. during kidney surgery including partial nephrectomy or kidney transplantation. Hyperspectral imaging (HSI) has great potential to objectively describe and quantify this perfusion as opposed to conventional surrogate techniques such as ultrasound flowmeter, indocyanine green or the subjective eye of the surgeon. An established live porcine model under general anesthesia received median laparotomy and renal mobilization. Different scenarios that were measured using HSI were (1) complete, (2) gradual and (3) partial malperfusion. The differences in spectral reflectance as well as HSI oxygenation (StO2) between different perfusion states were compelling and as high as 56.9% with 70.3% (± 11.0%) for "physiological" vs. 13.4% (± 3.1%) for "venous congestion". A machine learning (ML) algorithm was able to distinguish between these perfusion states with a balanced prediction accuracy of 97.8%. Data from this porcine study including 1300 recordings across 57 individuals was compared to a human dataset of 104 recordings across 17 individuals suggesting clinical transferability. Therefore, HSI is a highly promising tool for intraoperative microvascular evaluation of perfusion states with great advantages over existing surrogate techniques. Clinical trials are required to prove patient benefit. [ABSTRACT FROM AUTHOR]

Fine-grained classification of tree species by using high-spectral image data has garnered considerable attention from scholars. In this study, through field measurements from Maguan County, Wenshan Prefecture, Yunnan Province, China, high-spectral image data from the Chinese Resource-1 02D satellite were used as the data source. Various analyses were conducted on the original image's spectral curve, the spectral curve after envelope removal, the spectral curve after first-order differential transformation, and the spectral curve after second-order differential transformation. A spectral angle mapping classification method was employed to classify and identify four dominant tree species in Maguan County, and the accuracy of the classification results was validated using a confusion matrix. Results indicate that the highest accuracy in tree species classification was achieved when first-order differential transformation and envelope removal were used for the spectral curve; the overall accuracy exceeded 95%, and the kappa value was approximately 0.95. The classification results for the spectral curve after second-order differential transformation were the lowest, with an overall accuracy of 81.69% and a kappa value of 0.76. This research demonstrates that applying first-order differential transformation or envelope removal in combination with spectral angle mapping classification considerably reduces data processing time and improves tree species classification accuracy. [ABSTRACT FROM AUTHOR]

Abstract\nHIGHLIGHTSFruit and vegetables (F&V) are vastly complicated products with highly diverse chemical and structural characteristics. Advanced imaging techniques either combine imaging with spectral information or can provide excellent tissue penetration, and enable the possibility to target, visualize and even qualify the chemical and physical (structural) heterogeneity within F&V. In this review, visible and/or near infrared hyperspectral imaging, Fourier transform infrared microspectroscopic imaging, Raman imaging, X-ray and magnetic resonance imaging to reveal chemical and structural information in a spatial context of F&V at the macro- (entire products), meso- (tissues), and micro- (individual cells) scales are comprehensively summarized. In addition, their basic concepts and operational procedures, particularly sample preparation and instrumental parameter adjustments, are addressed. Finally, future challenges and perspectives of these techniques are put forward. These imaging techniques are powerful tools to assess the biochemical and structural heterogeneity of F&V. Cost reduction, sensor fusion and data sharing platforms are future trends. More emphasis on aspects of knowledge and extension at the level of academia and research, especially on how to select techniques, choose operational parameters and prepare samples, are important to overcome barriers for the wider adoption of these techniques to improve the evaluation of F&V quality.Hyperspectral imaging reveals chemical heterogeneity of fruit and vegetables.Imaging techniques provide spatial insights in fruit and vegetables at multiple scales.Future trends are cost reduction, sensor fusion and data sharing.Instrumental adjustment and sample preparation should receive more attention.Hyperspectral imaging reveals chemical heterogeneity of fruit and vegetables.Imaging techniques provide spatial insights in fruit and vegetables at multiple scales.Future trends are cost reduction, sensor fusion and data sharing.Instrumental adjustment and sample preparation should receive more attention. [ABSTRACT FROM AUTHOR]

Introduction: Modeling and mapping of soil organic carbon concentration and distribution at the pedon scale is a current knowledge gap that can be addressed by laboratory-based hyperspectral imaging and chemometric analysis of soil cores. Despite the advancements in soil organic carbon models based on hyperspectral images, it is not clear how these models will perform upon input with images at higher resolutions than those of their training sets. This study aims to measure the generalizability of a soil organic carbon model based on a test set with higher resolution hyperspectral images than that of its training set. Methods: Organic carbon contents were measured at 10 cm intervals on eight soil cores for use as the training set and at 1 cm intervals on a single core for use as the test set. Three regression models, namely, multilayer perceptron, partial least-squares, and support vector regressions, were trained and tested with the median of each hyperspectral image for each of these intervals as the training and test predictors. Permutation importance analysis was performed to explain the models. Results: The results show that although all three models had the same validation R2 of 0.92 for cross-validation on the 10 cm data, multilayer perceptron regression allowed the best generalization with a test R2 of 0.96 compared to the partial least-squares regression (0.81) and support vector regression (0.86). It was demonstrated that the multilayer perceptron model is more robust to soil surface anomalies and that it predicts soil organic carbon on the test set by learning the spectral features related to soil organic matter chromophore activity in the 950--1,150 nm region along with clay mineralogy derived from peaks at 1,400, 1,900, 2,200, 2,250, and 2,350 nm. Conclusions: This study shows that while the regression models based on hyperspectral images perform well at the 10-cm-resolution cross validation, multilayer perceptron regression shows superior generalization and robustness for a higher 1-cm-resolution test set without much loss of prediction power. [ABSTRACT FROM AUTHOR]

In the manufacturing process of electrical devices, ensuring the cleanliness of technical surfaces, such as direct bonded copper substrates, is crucial. An in-line monitoring system for quality checking must provide sufficiently resolved lateral data in a short time. UV hyperspectral imaging is a promising in-line method for rapid, contactless, and large-scale detection of contamination; thus, UV hyperspectral imaging (225–400 nm) was utilized to characterize the cleanliness of direct bonded copper in a non-destructive way. In total, 11 levels of cleanliness were prepared, and a total of 44 samples were measured to develop multivariate models for characterizing and predicting the cleanliness levels. The setup included a pushbroom imager, a deuterium lamp, and a conveyor belt for laterally resolved measurements of copper surfaces. A principal component analysis (PCA) model effectively differentiated among the sample types based on the first two principal components with approximately 100.0% explained variance. A partial least squares regression (PLS-R) model to determine the optimal sonication time showed reliable performance, with R2cv = 0.928 and RMSECV = 0.849. This model was able to predict the cleanliness of each pixel in a testing sample set, exemplifying a step in the manufacturing process of direct bonded copper substrates. Combined with multivariate data modeling, the in-line UV prototype system demonstrates a significant potential for further advancement towards its application in real-world, large-scale processes. [ABSTRACT FROM AUTHOR]

In the field of rice processing and cultivation, it is crucial to adopt efficient, rapid and user-friendly techniques to detect the flavor values of various rice varieties. The conventional methods for flavor value assessment mainly rely on chemical analysis and technical evaluation, which not only deplete the rice resources but also incur significant time and labor costs. In this study, hyperspectral imaging technology was utilized in combination with an improved Particle Swarm Optimization Support Vector Machine (PSO-SVM) algorithm, i.e., the Grid Iterative Search Particle Swarm Optimization Support Vector Machine (GISPSO-SVM) algorithm, introducing a new non-destructive technique to determine the flavor value of rice. The method captures the hyperspectral feature data of different rice varieties through image acquisition, preprocessing and feature extraction, and then uses these features to train a model using an optimized machine learning algorithm. The results show that the introduction of GIS algorithms in a PSO-optimized SVM is very effective and can improve the parameter finding ability. In terms of flavor value prediction accuracy, the Principal Component Analysis (PCA) combined with the GISPSO-SVM algorithm achieved 96% accuracy, which was higher than the 93% of the Competitive Adaptive Weighted Sampling (CARS) algorithm. And the introduction of the GIS algorithm in different feature selection can improve the accuracy to different degrees. This novel approach helps to evaluate the flavor values of new rice varieties non-destructively and provides a new perspective for future rice flavor value detection methods. [ABSTRACT FROM AUTHOR]

Varroa mite infestation poses a severe threat to honeybee colonies globally. This study investigates the feasibility of utilizing the HS-Cam and machine learning techniques for Varroa mite counting. The methodology involves image acquisition, dimensionality reduction through Principal Component Analysis (PCA), and machine learning-based segmentation and classification algorithms. Specifically, a k-Nearest Neighbors (kNNs) model distinguishes Varroa mites from other objects in the images, while a Support Vector Machine (SVM) classifier enhances shape detection. The final phase integrates a dedicated counting algorithm, leveraging outputs from the SVM classifier to quantify Varroa mite populations in hyperspectral images. The preliminary results demonstrate segmentation accuracy exceeding 99% and an average precision of 0.9983 and recall of 0.9947 across all the classes. The results obtained from our machine learning-based approach for Varroa mite counting were compared against ground-truth labels obtained through manual counting, demonstrating a high degree of agreement between the automated counting and manual ground truth. Despite working with a limited dataset, the HS-Cam showcases its potential for Varroa counting, delivering superior performance compared to traditional RGB images. Future research directions include validating the proposed hyperspectral imaging methodology with a more extensive and diverse dataset. Additionally, the effectiveness of using a near-infrared (NIR) excitation source for Varroa detection will be explored, along with assessing smartphone integration feasibility. [ABSTRACT FROM AUTHOR]

Locality preserving projection (LPP) is a typical feature extraction method based on spectral information for hyperspectral image (HSI) classification. Recently, to improve the classification performance, the spatial information of HSI has been applied in the LPP method. However, for most of spatial–spectral‐based LPP methods, they explore the spatial–spectral information within a fixed local window, which cannot be appropriate to the irregular‐shape ground objects in HSI. To over this issue, an effective superpixel‐guided LPP and spatial–spectral classification method are proposed, in which the spatial–adaptive structure information is fully excavated for HSI classification. Specifically, superpixel segmentation is first conducted on the HSI to generate shape‐adaptive homogeneous subregions. Then, to learn more discriminative projection, the neighbourhood graph for LPP is constructed based on spatial–spectral similarity, in which pixels within the same superpixel are connected. Finally, the obtained projection feature is input a classifier to yield the initial classification result, and the edge information of ground objects captured by superpixels is utilized to optimize the initial classification result. Experiments on two real hyperspectral datasets demonstrate that the proposed superpixel‐guided and spatial–spectral classification method significantly outperforms the other well‐known techniques for HSI classification. [ABSTRACT FROM AUTHOR]

Aims Main Methods Key Findings Significance To establish a system based on hyperspectral imaging and deep learning for the detection of cancer cells in metastatic lymph nodes.The continuous sections of metastatic lymph nodes from 45 oral squamous cell carcinoma (OSCC) patients were collected. An improved ResUNet algorithm was established for deep learning to analyze the spectral curve differences between cancer cells and lymphocytes, and that between tumor tissue and normal tissue.It was found that cancer cells, lymphocytes, and erythrocytes in the metastatic lymph nodes could be distinguished basing hyperspectral image, with overall accuracy (OA) as 87.30% and average accuracy (AA) as 85.46%. Cancerous area could be recognized by hyperspectral image and deep learning, and the average intersection over union (IOU) and accuracy were 0.6253 and 0.7692, respectively.This study indicated that deep learning‐based hyperspectral techniques can identify tumor tissue in OSCC metastatic lymph nodes, achieving high accuracy of pathological diagnosis, high work efficiency, and reducing work burden. But these are preliminary results limited to a small sample. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging has emerged as a pivotal technology in agricultural research, offering a powerful means to non-invasively monitor stress factors, such as drought, in crops like potato plants. In this context, the integration of attention-based deep learning models presents a promising avenue for enhancing the efficiency of stress detection, by enabling the identification of meaningful spectral channels. This study assesses the performance of deep learning models on two potato plant cultivars exposed to water-deficient conditions. It explores how various sampling strategies and biases impact the classification metrics by using a dual-sensor hyperspectral imaging systems (VNIR -Visible and Near-Infrared and SWIR—Short-Wave Infrared). Moreover, it focuses on pinpointing crucial wavelengths within the concatenated images indicative of water-deficient conditions. The proposed deep learning model yields encouraging results. In the context of binary classification, it achieved an area under the receiver operating characteristic curve (AUC-ROC—Area Under the Receiver Operating Characteristic Curve) of 0.74 (95% CI: 0.70, 0.78) and 0.64 (95% CI: 0.56, 0.69) for the KIS Krka and KIS Savinja varieties, respectively. Moreover, the corresponding F1 scores were 0.67 (95% CI: 0.64, 0.71) and 0.63 (95% CI: 0.56, 0.68). An evaluation of the performance of the datasets with deliberately introduced biases consistently demonstrated superior results in comparison to their non-biased equivalents. Notably, the ROC-AUC values exhibited significant improvements, registering a maximum increase of 10.8% for KIS Krka and 18.9% for KIS Savinja. The wavelengths of greatest significance were observed in the ranges of 475–580 nm, 660–730 nm, 940–970 nm, 1420–1510 nm, 1875–2040 nm, and 2350–2480 nm. These findings suggest that discerning between the two treatments is attainable, despite the absence of prominently manifested symptoms of drought stress in either cultivar through visual observation. The research outcomes carry significant implications for both precision agriculture and potato breeding. In precision agriculture, precise water monitoring enhances resource allocation, irrigation, yield, and loss prevention. Hyperspectral imaging holds potential to expedite drought-tolerant cultivar selection, thereby streamlining breeding for resilient potatoes adaptable to shifting climates. [ABSTRACT FROM AUTHOR]

We advanced the practical development of compressive hyperspectral cameras for remote sensing scenarios with a design that simultaneously compresses and captures high-quality spectral information of a scene via configurable measurements. We built a prototype imaging system that is compatible with light-modulation devices that encode the incoming spectrum. The sensing approach enables a substantial reduction in the volume of data collected and transmitted, facilitating large-scale remote hyperspectral imaging. A main advantage of our sensing design is that it allows for adaptive sampling. When prior information of a survey region is available or gained, the modulation patterns can be re-programmed to efficiently sample and detect desired endmembers. Given target spectral signatures, we propose an optimization scheme that guides the encoding process. The approach severely reduces the number of required sampling patterns, with the ability to achieve image segmentation and correct distortions. Additionally, to decode the modulated data, we considered a novel reconstruction algorithm suited for large-scale images. The computational methodology leverages the multidimensional structure and redundant representation of hyperspectral images via the canonical polyadic decomposition of multiway arrays. Under realistic remote sensing scenarios, we demonstrated the efficiency of our approach with several data sets collected by our prototype camera and reconstructed by our low-rank tensor decoder. [ABSTRACT FROM AUTHOR]

Only 30% of embryos from in vitro fertilized oocytes successfully implant and develop to term, leading to repeated transfer cycles. To reduce time-to-pregnancy and stress for patients, there is a need for a diagnostic tool to better select embryos and oocytes based on their physiology. The current standard employs brightfield imaging, which provides limited physiological information. Here, we introduce METAPHOR: Metabolic Evaluation through Phasor-based Hyperspectral Imaging and Organelle Recognition. This non-invasive, label-free imaging method combines two-photon illumination and AI to deliver the metabolic profile of embryos and oocytes based on intrinsic autofluorescence signals. We used it to classify i) mouse blastocysts cultured under standard conditions or with depletion of selected metabolites (glucose, pyruvate, lactate); and ii) oocytes from young and old mouse females, or in vitro-aged oocytes. The imaging process was safe for blastocysts and oocytes. The METAPHOR classification of control vs. metabolites-depleted embryos reached an area under the ROC curve (AUC) of 93.7%, compared to 51% achieved for human grading using brightfield imaging. The binary classification of young vs. old/in vitro-aged oocytes and their blastulation prediction using METAPHOR reached an AUC of 96.2% and 82.2%, respectively. Finally, organelle recognition and segmentation based on the flavin adenine dinucleotide signal revealed that quantification of mitochondria size and distribution can be used as a biomarker to classify oocytes and embryos. The performance and safety of the method highlight the accuracy of noninvasive metabolic imaging as a complementary approach to evaluate oocytes and embryos based on their physiology. [ABSTRACT FROM AUTHOR]

Drought is a major factor affecting crops, thus efforts are needed to increase plant resilience to this abiotic stress. The overlapping signaling pathways between drought and cell wall integrity maintenance responses create a possibility of increasing drought resistance by modifying cell walls. Here, using herbaceous and woody plant model species, Arabidopsis and hybrid aspen, respectively, we investigated how the integrity of xylan in secondary walls affects the responses of plants to drought stress. Plants, in which secondary wall xylan integrity was reduced by expressing fungal GH10 and GH11 xylanases or by affecting genes involved in xylan backbone biosynthesis, were subjected to controlled drought while their physiological responses were continuously monitored by RGB, fluorescence, and/or hyperspectral cameras. For Arabidopsis, this was supplemented with survival test after complete water withdrawal and analyses of stomatal function and stem conductivity. All Arabidopsis xylan-impaired lines showed better survival upon complete watering withdrawal, increased stomatal density and delayed growth inhibition by moderate drought, indicating increased resilience to moderate drought associated with modified xylan integrity. Subtle differences were recorded between xylan biosynthesis mutants (irx9, irx10 and irx14) and xylanaseexpressing lines. irx14 was the most drought resistant genotype, and the only genotype with increased lignin content and unaltered xylem conductivity despite its irx phenotype. Rosette growth was more affected by drought in GH11- than in GH10-expressing plants. In aspen, mild downregulation of GT43B and C genes did not affect drought responses and the transgenic plants grew better than the wild-type in drought and well-watered conditions. Both GH10 and GH11 xylanases strongly inhibited stem elongation and root growth in well-watered conditions but growth was less inhibited by drought in GH11-expressing plants than in wild-type. Overall, plants with xylan integrity impairment in secondary walls were less affected than wild-type by moderately reduced water availability but their responses also varied among genotypes and species. Thus, modifying the secondary cell wall integrity can be considered as a potential strategy for developing crops better suited to withstand water scarcity, but more research is needed to address the underlying molecular causes of this variability. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging was used to predict the total polyphenol content in low-temperature stressed tomato seedlings for the development of a multispectral image sensor. The spectral data with a full width at half maximum (FWHM) of 5 nm were merged to obtain FWHMs of 10 nm, 25 nm, and 50 nm using a commercialized bandpass filter. Using the permutation importance method and regression coefficients, we developed the least absolute shrinkage and selection operator (Lasso) regression models by setting the band number to ≥11, ≤10, and ≤5 for each FWHM. The regression model using 56 bands with an FWHM of 5 nm resulted in an R2 of 0.71, an RMSE of 3.99 mg/g, and an RE of 9.04%, whereas the model developed using the spectral data of only 5 bands with a FWHM of 25 nm (at 519.5 nm, 620.1 nm, 660.3 nm, 719.8 nm, and 980.3 nm) provided an R2 of 0.62, an RMSE of 4.54 mg/g, and an RE of 10.3%. These results show that a multispectral image sensor can be developed to predict the total polyphenol content of tomato seedlings subjected to low-temperature stress, paving the way for energy saving and low-temperature stress damage prevention in vegetable seedling production. [ABSTRACT FROM AUTHOR]