Hyperspectral Imaging Technique Research Articles

Deep learning algorithm development for early detection of Botrytis cinerea infected strawberry fruit using hyperspectral fluorescence imaging

Botrytis cinerea is a strawberry disease that causes economic loss worldwide. If a disease outbreak occurs during storage or transportation, it can spread rapidly to neighboring objects; thus, there is a need to develop early diagnostic techniques to prevent it. In this study, we developed a method to rapidly and nondestructively determine the infection stage in strawberry fruit using hyperspectral fluorescence imaging. ‘Keumsil’ cultivar strawberries were used, and hyperspectral fluorescence images were acquired over 144 h in control and inoculation groups. Strawberries were categorized into four infection stages based on visible mold spores: healthy, asymptomatic, infected, and after-infected. Hyperspectral fluorescence spectra were extracted to develop a one-dimensional convolutional neural network (1D-CNN) model based on partial least squares-discriminant analysis (PLS-DA), VGG-19, and ResNet-50; data augmentation techniques and six spectral preprocessing techniques were applied to the datasets. The application of data augmentation techniques improved the performances of the PLS-DA and 1D-CNN models in determining the infection stage. The performance of the ResNet-50-based 1D-CNN model with mean normalization data and data augmentation technique was the best, with 96.88% precision, 96.87% recall, 96.85% F1-score, and 96.86% accuracy. The results of this study showed that it is possible to determine the infection stage of Botrytis cinerea on strawberry fruit using hyperspectral fluorescence imaging and 1D-CNN techniques. This technology is expected to be applied for the early detection of Botrytis cinerea in strawberry growth, postharvest sorting and packing, and distribution stages.

Rapid determination of starch and alcohol contents in fermented grains by hyperspectral imaging combined with data fusion techniques.

Starch and alcohol serve as pivotal indicators in assessing the quality of lees fermentation. In this paper, two hyperspectral imaging (HSI) techniques (visible-near-infrared (Vis-NIR) and NIR) were utilized to acquire separate HSI data, which were then fused and analyzed toforecast the starch and alcohol contents during the fermentation of lees. Five preprocessing methods were first used to preprocess the Vis-NIR, NIR, and the fused Vis-NIR and NIR data, after which partial least squares regression models were established to determine the best preprocessing method. Following, competitive adaptive reweighted sampling, successive projection algorithm, and principal component analysis algorithms were used to extract the characteristic wavelengths to accurately predict the starch and alcohol levels. Finally, support vector machine (SVM)-AdaBoost and XGBoost models were built based on the low-level fusion (LLF) and intermediate-level fusion (ILF) of single Vis-NIR and NIR as well as the fused data. The results showed that the SVM-AdaBoost model built using the LLF data afterpreprocessing by standard normalized variable was most accurate for predicting the starch content, with an of 0.9976 and a root mean square error of prediction (RMSEP) of 0.0992. The XGBoost model built using ILF data was most accurate for predicting the alcohol content, with an of 0.9969 and an RMSEP of 0.0605. In conclusion, the analysis of fused data from distinct HSI technologies facilitates rapid and precise determination of the starch and alcohol contents in fermented grains.

Detection of sweet corn seed viability based on hyperspectral imaging combined with firefly algorithm optimized deep learning

Detection of sweet corn seed viability based on hyperspectral imaging combined with firefly algorithm optimized deep learning

Comparing visible and near infrared ‘point’ spectroscopy and hyperspectral imaging techniques to visualize the variability of apple firmness

In this work, visible and near-infrared ‘point’ (Vis-NIR) spectroscopy and hyperspectral imaging (Vis-NIR-HSI) techniques were applied on three different apple cultivars to compare their firmness prediction performances based on a large intra-variability of individual fruit, and develop rapid and simple models to visualize the variability of apple firmness on three apple cultivars. Apples with high degree of intra-variability can strongly affect the prediction model performances. The apple firmness prediction accuracy can be improved based on the large intra-variability samples with the coefficient variation (CV) values over 10%. The least squares-support vector machine (LS-SVM) models based on Vis-NIR-HSI spectra had better performances for firmness prediction than that of Vis-NIR spectroscopy, with the with the Rc2 over 0.84. Finally, The Vis-NIR-HSI technique combined with least squares-support vector machine (LS-SVM) models were successfully applied to visualize the spatial the variability of apple firmness.

Treating wastewater for microplastics to a level on par with nearby marine waters

Retention of microplastics (MPs) at the third largest wastewater treatment plant (WWTP) in Sweden was investigated. The plant is one of the most modern and advanced of its kind, with rapid sand filter for tertiary treatment in combination with mechanical, biological, and chemical treatment. It achieved a significantly high treatment efficiency, which brought the MP concentration in its discharge on par with concentrations measured in marine waters of the same region. This novel data shows that properly designed modern WWTPs can reduce the MP content of sewage down to background levels measured in the receiving aquatic environment. Opposite to current understanding of the retention of MP by WWTPs, a modern and well-designed WWTP does not have to be a significant point source for MP. MPs were quantified at all major treatment steps, including digester inlet and outlet sludge. MPs sized 10–500 µm were analyzed by a focal plane array based micro-Fourier transform infrared (FPA-µFTIR) microscopy, a hyperspectral imaging technique, while MPs above 500 µm were analyzed by Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. Mass was estimated from the hyperspectral images for MPs <500 µm and from microscope images >500 µm. The overall treatment efficiency was in terms of MP counts 99.98 %, with a daily input of 6.42 × 1010 and output of 1.04 × 107 particles. The mass removal efficiency was 99.99 %. The mechanical part of the treatment, the pre-treatment, and primary stages, reduced both the MP counts and mass by approximately 71 %. The combined biological treatment, secondary settling, and final polishing with rapid sand filtration removed nearly all the remaining 29 %. MPs became successively smaller as they passed the different treatment steps. The digester inlet received 1.04 × 1011 MPs daily, while it discharged 9.96 × 1010 MPs, causing a small but not significant decrease in MP counts, with a corresponding MP mass reduction of 9.56 %.

Hyperspectral imaging for the detection of plant pathogens in seeds: recent developments and challenges.

Food security, a critical concern amid global population growth, faces challenges in sustainable agricultural production due to significant yield losses caused by plant diseases, with a multitude of them caused by seedborne plant pathogen. With the expansion of the international seed market with global movement of this propagative plant material, and considering that about 90% of economically important crops grown from seeds, seed pathology emerged as an important discipline. Seed health testing is presently part of quality analysis and carried out by seed enterprises and governmental institutions looking forward to exclude a new pathogen in a country or site. The development of seedborne pathogens detection methods has been following the plant pathogen detection and diagnosis advances, from the use of cultivation on semi-selective media, to antibodies and DNA-based techniques. Hyperspectral imaging (HSI) associated with artificial intelligence can be considered the new frontier for seedborne pathogen detection with high accuracy in discriminating infected from healthy seeds. The development of the process consists of standardization of methods and protocols with the validation of spectral signatures for presence and incidence of contamined seeds. Concurrently, epidemiological studies correlating this information with disease outbreaks would help in determining the acceptable thresholds of seed contamination. Despite the high costs of equipment and the necessity for interdisciplinary collaboration, it is anticipated that health seed certifying programs and seed suppliers will benefit from the adoption of HSI techniques in the near future.

Nerve Detection and Visualization Using Hyperspectral Imaging for Surgical Guidance.

During surgery of delicate regions, differentiation between nerve and surrounding tissue is crucial. Hyperspectral imaging (HSI) techniques can enhance the contrast between types of tissue beyond what the human eye can differentiate. Whereas an RGB image captures 3 bands within the visible light range (e.g., 400 nm to 700 nm), HSI can acquire many bands in wavelength increments that highlight regions of an image across a wavelength spectrum. We developed a workflow to identify nerve tissues from other similar tissues such as fat, bone, and muscle. Our workflow uses spectral angle mapper (SAM) and endmember selection. The method is robust for different types of environment and lighting conditions. We validated our workflow on two samples of human tissues. We used a compact HSI system that can image from 400 to 1700 nm to produce HSI of the samples. On these two samples, we achieved an intersection-over-union (IoU) segmentation score of 84.15% and 76.73%, respectively. We showed that our workflow identifies nerve segments that are not easily seen in RGB images. This method is fast, does not rely on special hardware, and can be applied in real time. The hyperspectral imaging and nerve detection approach may provide a powerful tool for image-guided surgery.

Establishment of deep learning model for the growth of tea cutting seedlings based on hyperspectral imaging technique

The shoot and root growth not only serve as important indicators for measuring the quality of tea cutting seedlings, but also can provide scientific basis for agricultural production and fine seedling management. Currently, manual weighing is the main method used to measure shoot and root growth, which is both destructive and inefficient. The study proposes a method for monitoring the shoot and root growth in tea cutting seedlings using hyperspectral imaging. First, mature leaves and shoots spectra were extracted from tea cutting seedlings using Mask R-CNN (Epoch=20 and Learning-rate=0.001). The spectra were then preprocessed by MSC, S-G, 1-D filtering techniques, with feature bands screened by UVE (Optimal factor number=5), CARS (Monte Carlo sampling times=300), and SPA (Epoch=25). Finally, a CNN-GRU (Epoch=100 and Learning-rate=0.01) network was employed to predict shoot and root biomass, and compared with machine learning methods of SVR (Kernel function=Polynomial), RFR (Ntrees=200), and PLSR (Latent variable=16) and deep learning methods (Epoch=100 and Learning-rate=0.01) of CNN and LSTM. The results show that (i) Mask R-CNN can accurately extract the spectral of mature leaves (precision=97.8 %) and shoots (precision=91.5 %), (ii) The spectral feature bands of shoot (number=212) and root (number=105) biomass screened by UVE were more abundant than CARS and SPA, (iii) The UVE+CNN-GRU model (Rp2=0.90, RMSEP = 0.12, RPD=2.43) based on shoots spectrum provided the optimal estimation of shoot biomass. The SPA+LSTM model (Rp2 = 0.65, RMSEP = 0.05, RPD=1.67) based on the mature leaves spectrum provided the optimal estimation of root biomass. These findings demonstrate that hyperspectral imaging technology combined with deep learning algorithms can accurately monitor the growth of cutting seedlings quickly without causing damage. This not only provides a new data sources along with technical means for efficient screening excellent tea varieties, but also improves agricultural production efficiency and resource utilization.

Deoxynivalenol Detection beyond the Limit in Wheat Flour Based on the Fluorescence Hyperspectral Imaging Technique.

Deoxynivalenol (DON) is a harmful fungal toxin, and its contamination in wheat flour poses a food safety concern globally. This study proposes the combination of fluorescence hyperspectral imaging (FHSI) and qualitative discrimination methods for the detection of excessive DON content in wheat flour. Wheat flour samples were prepared with varying DON concentrations through the addition of trace amounts of DON using the wet mixing method for fluorescence hyperspectral image collection. SG smoothing and normalization algorithms were applied for original spectra preprocessing. Feature band selection was carried out by applying the successive projection algorithm (SPA), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the random frog algorithm on the fluorescence spectrum. Random forest (RF) and support vector machine (SVM) classification models were utilized to identify wheat flour samples with DON concentrations higher than 1 mg/kg. The results indicate that the SG-CARS-RF and SG-CARS-SVM models showed better performance than other models, achieving the highest recall rate of 98.95% and the highest accuracy of 97.78%, respectively. Additionally, the ROC curves demonstrated higher robustness on the RF algorithm. Deep learning algorithms were also applied to identify the samples that exceeded safety standards, and the convolutional neural network (CNN) model achieved a recognition accuracy rate of 97.78% for the test set. In conclusion, this study demonstrates the feasibility and potential of the FHSI technique in detecting DON infection in wheat flour.

Non-destructive detection of defective maize kernels using hyperspectral imaging and convolutional neural network with attention module

Rapid, effective and non-destructive detection of the defective maize kernels is crucial for their high-quality storage in granary. Hyperspectral imaging (HSI) coupled with convolutional neural network (CNN) based on spectral and spatial attention (Spl-Spal-At) module was proposed for identifying the different types of maize kernels. The HSI data within 380–1000 nm of six classes of sprouted, heat-damaged, insect-damaged, moldy, broken and healthy kernels was collected. The CNN-Spl-At, CNN-Spal-At and CNN-Spl-Spal-At models were established based on the spectra, images and their fusion features as inputs for the recognition of different kernels. Further compared the performances of proposed models and conventional models were built by support vector machine (SVM) and extreme learning machine (ELM). The results indicated that the recognition ability of CNN with attention series models was significantly better than that of SVM and ELM models and fused features were more conducive to expressing the appearance of different kernels than single features. And the CNN-Spl-Spal-At model had an optimal recognition result with high average classification accuracy of 98.04 % and 94.56 % for the training and testing sets, respectively. The recognition results were visually presented on the surface image of kernels with different colors. The CNN-Spl-Spal-At model was built in this study could effectively detect defective maize kernels, and it also had great potential to provide the analysis approaches for the development of non-destructive testing equipment based on HSI technique for maize quality.

Determination of hydration kinetic of pinto beans: A hyperspectral images application

Hydration is a typical operation applied to legumes before cooking, reducing time and the associated energy cost. To monitor the process, mass balance method is the most used methodology, despite this method is destructive, repetitive, and time-consuming. For that reason. hyperspectral techniques are presented as an alternative for assessing the hydration process since it is a noninvasive method. Therefore, the objective of this work was to evaluate the technique of hyperspectral imaging for studying the hydration kinetics of pinto beans. For this purpose, a sample of pinto beans was hydrated in distilled water, determining moisture content during the process and taking hyperspectral images by reflectance mode, in the range 400 to 800 nm until constant mass. The moisture content was modelled using Peleg and a sigmoidal model. Next, the images were pre-treated and the median spectral profile for each bean was obtained. Then, a regression model was fitted, using the wavelength that maximized the coefficient of determination (R2) and minimized the root mean square error (RMSE). The results show that Peleg model fit experimental data with R2 in the range of 0.974 to 0.989 while sigmoidal model of 0.997 to 0.999. On other hand, mean spectral profiles at 632 nm and sigmoidal model give the higher metrics 0.997 and 38.3 for R2 and RMSE respectively. The results showed that hyperspectral imaging in reflectance mode is a tool capable of measuring the hydration level of beans with higher performance at 632 nm, with a determination coefficient R2 higher than 0.98.

Prediction of Soluble-Solid Content in Citrus Fruit Using Visible-Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm.

Citrus fruits were sorted based on external qualities, such as size, weight, and color, and internal qualities, such as soluble solid content (SSC), acidity, and firmness. Visible and near-infrared (VNIR) hyperspectral imaging techniques were used as rapid and nondestructive techniques for determining the internal quality of fruits. The applicability of the VNIR hyperspectral imaging technique for predicting the SSC in citrus fruits was evaluated in this study. A VNIR hyperspectral imaging system with a wavelength range of 400-1000 nm and 100 W light source was used to acquire hyperspectral images from citrus fruits in two orientations (i.e., stem and calyx ends). The SSC prediction model was developed using partial least-squares regression (PLSR). Spectrum preprocessing, effective wavelength selection through competitive adaptive reweighted sampling (CARS), and outlier detection were used to improve the model performance. The performance of each model was evaluated using the coefficient of determination (R2) and root mean square error (RMSE). In the present study, the PLSR model was developed using only a citrus cultivar. The SSC prediction CARS-PLSR model with outliers removed exhibited R2 and RMSE values of approximatively 0.75 and 0.56 °Brix, respectively. The results of this study are expected to be useful in similar fields such as agricultural and food post-harvest management, as well as in the development of an online system for determining the SSC of citrus fruits.

Almond defect and freshness inspection system using hyperspectral imaging and deep learning techniques

Almonds, recognized as a nutritious and low-calorie snack rich in unsaturated fats and essential nutrients, are primarily imported from California. Despite undergoing grading control by the United States Department of Agriculture (USDA), concerns persist regarding the presence of defective kernels. Manual hand-picking is the conventional method for almond processing; however, prolonged picking can reduce accuracy due to visual fatigue. Consumers prioritize freshness due to fungi’s potential for aflatoxin production, emphasizing the need for vigilant almond consumption. In response to these challenges, this study presents an innovative approach that employs a lightweight VGG16 and an enhanced MobileNet model for identifying defects based on RGB images of almonds. This method effectively classifies broken, scratched, and malformed kernels. Additionally, hyperspectral imaging (HSI) data in the 400–1000 nm range were collected to distinguish insect-damaged from undamaged kernels and assess almond freshness. For comparative analysis, various band selection algorithms were applied to extract distinct spectral bands. For insect damage identification, we propose a merged CNN model created by simultaneously combining 1D- and 2D-CNNs, allowing for the concurrent extraction of spectral and spatial characteristics. This approach achieved a remarkable ACC of 0.9905 and a Kappa value of 0.9521. Meanwhile, in evaluating freshness, PCA was employed to select the first 30 principal components as vital bands. Our proposed lightweight 1D-CNN delivered an ACC of 0.9768 and a Kappa value of 0.9527 in freshness assessment.

Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning

Developing a fast and non-destructive methodology to identify the storage years of Coix seed is important in safeguarding consumer well-being. This study employed the utilization of hyperspectral imaging (HSI) in conjunction with conventional machine learning techniques such as support vector machines (SVM), k-nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), as well as the deep learning method of residual neural network (ResNet), to establish identification models for Coix seed samples from different storage years. Under the fusion-based modeling approach, the model’s classification accuracy surpasses that of visible to near infrared (VNIR) and short-wave infrared (SWIR) spectral modeling individually. The classification accuracy of the ResNet model and SVM exceeds that of other conventional machine learning models (KNN, RF, and XGBoost). Redundant variables were further diminished through competitive adaptive reweighted sampling feature wavelength screening, which had less impact on the model’s accuracy. Upon validating the model’s performance using an external validation set, the ResNet model yielded more satisfactory outcomes, exhibiting recognition accuracy exceeding 85%. In conclusion, the comprehensive results demonstrate that the integration of deep learning with HSI techniques effectively distinguishes Coix seed samples from different storage years.

Non-destructive identification of Pseudostellaria heterophylla from different geographical origins by Vis/NIR and SWIR hyperspectral imaging techniques.

The composition of Pseudostellaria heterophylla (Tai-Zi-Shen, TZS) is greatly influenced by the growing area of the plants, making it significant to distinguish the origins of TZS. However, traditional methods for TZS origin identification are time-consuming, laborious, and destructive. To address this, two or three TZS accessions were selected from four different regions of China, with each of these resources including distinct quality grades of TZS samples. The visible near-infrared (Vis/NIR) and short-wave infrared (SWIR) hyperspectral information from these samples were then collected. Fast and high-precision methods to identify the origins of TZS were developed by combining various preprocessing algorithms, feature band extraction algorithms (CARS and SPA), traditional two-stage machine learning classifiers (PLS-DA, SVM, and RF), and an end-to-end deep learning classifier (DCNN). Specifically, SWIR hyperspectral information outperformed Vis/NIR hyperspectral information in detecting geographic origins of TZS. The SPA algorithm proved particularly effective in extracting SWIR information that was highly correlated with the origins of TZS. The corresponding FD-SPA-SVM model reduced the number of bands by 77.2% and improved the model accuracy from 97.6% to 98.1% compared to the full-band FD-SVM model. Overall, two sets of fast and high-precision models, SWIR-FD-SPA-SVM and SWIR-FD-DCNN, were established, achieving accuracies of 98.1% and 98.7% respectively. This work provides a potentially efficient alternative for rapidly detecting the origins of TZS during actual production.

Understanding the changes in millable oil content and fatty acid composition of infrared radiation treated mustard (Brassica juncea) seeds using Vis-NIR-SWIR hyperspectral imaging

Enhancing the recovery of edible oil during mechanical oil expression offers a potential solution to mitigate the gap between the supply and demand. Infrared (IR) pre-treatment has demonstrated an enhanced availability of mechanically extractable oil from mustard seeds. In this study, hyperspectral imaging (HSI) techniques were employed to investigate the spatial distribution of oil in mustard seeds subjected to IR treatment under varying voltage and time of exposure. Both visible near-infrared (Vis-NIR, 399–1003 nm) and short-wave infrared (SWIR, 895–1712 nm) HSI systems were utilized to acquire spectral data followed by chemometric analysis, including partial least square discriminant analysis (PLS-DA) and regression (PLSR), to develop prediction models. The PLS-DA model exhibited robust classification capabilities for the differentiating mustard seeds based on various IR treatments from control samples. Notably, the accuracy levels achieved were 94.7 % for Vis-NIR-HSI and 99.2 % for SWIR-HSI. The prediction of oil content and fatty acid components, such as erucic acid, oleic acid, saturated fatty acids and mono fatty acids (MUFAs) could be carried out by the PLSR model developed using SWIR-HSI spectral data (R2 > 0.80). These predictions closely aligned with the outcomes obtained from analytical techniques. However, when utilizing Vis-NIR spectral data, the predictions (R2 > 0.65) were comparatively less accurate.

Research on Tree Distribution in the Mt. Hambeak Area of Taebaek City Using Hyperspectral Image-LiDAR in 2019-2020

This research was conducted to improve the vegetation survey method using hyperspectral imaging and LiDAR techniques. Using Ocean FX, spectral data of seven representative species of Mount Hambaek were acquired, and hyperspectral image data of Mount Hambaek were acquired using AisaFENIX 1K and microCASI-1920 sensors. For spectral data and hyperspectral image data, tree species data were extracted using the Spectral Angle Mapper (SAM) technique, and data such as tree species location, height, and diameter at breast height were extracted through LiDAR data. the results of an investigation A total of 39,351 trees were surveyed in the Mount Hambuk area, with 25,930 trees (65.9%) in &lt;i&gt;Quercus mongolica&lt;/i&gt;, followed by &lt;i&gt;Larix kaempferi&lt;/i&gt; with 6,805 trees (17.3%), &lt;i&gt;Alnus sibirica&lt;/i&gt; with 3,625 trees (9.2%), &lt;i&gt;Pinus dendiflora&lt;/i&gt; 1,764 trees (4.5%), &lt;i&gt;Pinus koraiensis&lt;/i&gt; 605 trees (1.5%), &lt;i&gt;Pinus rigida&lt;/i&gt; 405 trees (1.0%), and &lt;i&gt;Betulaermanii&lt;/i&gt; 217 trees (0.5%), As a result of selecting 28 representative colonies to be surveyed and conducting on-site verification, 27 out of 28 colonies were found to be 96.43% accurate.

Assessing Soil Nutrient Content and Mapping in Tropical Tamil Nadu, India, through Precursors IperSpettrale Della Mission Applicative Hyperspectral Spectroscopy

Remote sensing techniques are capable of mapping soil nutrient concentrations and preparing soil maps for long-term agricultural productivity and food security. Recently, hyperspectral imaging techniques have been widely used to quantify and map nitrogen levels in the soil in large areas. In this study, we employed a partial least square regression (PLSR) technique over PRISMA hyperspectral data on part of the Radhapuram area, Tirunelveli District, India to improve the accuracy of estimating soil nutrient levels. The results of the study show that the PLSR prediction accuracy rates using field observations provided the most accurate estimates of soil containing organic carbon (OC), available nitrogen (AN), available phosphorus (AP), and available potassium (AK). Soil nutrient predictions were carried out using bands in visible near-infrared and near-infrared regions. Analysis of 150 bands using random analyses provided an R2 value of 0.970 and the PLSR technique performed best while using the combined bands in the VNIR+NIR regions. Based on the analyses, PRISMA hyperspectral images using spectral angle mapper (SAM) image classification provided a better map of the soil consisting of organic carbon. The research findings are important references for the prediction of soil nutrients with high accuracy.

HYPERSPECTRAL IMAGING IN PRESERVATION OF CROATIA’S HISTORIC TREES: A CASE STUDY OF DEDEK OAK IN MAKSIMIR PARK

Abstract. This study focuses on the application of hyperspectral imaging techniques for the genetic preservation of historic trees, specifically investigating the 600-year-old "Dedek" Oak in Maksimir Park, Croatia. The research aims to contribute to the conservation of Croatia's natural heritage and underscore the potential of hyperspectral imaging in tree preservation. The Croatian Forest Research Institute has been actively involved in preserving the pedunculate oak trees, recognizing their cultural and ecological significance. Efforts have been made to cultivate seedlings from the original genetic material, thereby preventing the loss of valuable genes and ensuring their long-term preservation. Spectral signatures of "Dedek" Oak and its clones were captured using high-performance hyperspectral push broom cameras, including the HySpex VNIR-1800 and SWIR-384. Spectral camera scanning was conducted in the field and laboratory. The comparative analysis of the spectral samples revealed unique characteristics and demonstrated the effectiveness of hyperspectral imaging in studying historic trees for preservation purposes. The spectral signatures of vegetation display dynamic characteristics in terms of spectral resolution. Collecting and documenting these signatures is considerably more challenging, and their integration into spectral libraries should be approached with careful consideration. There are several spectral libraries that are organized by chapters and consist of samples that have a sufficient number of analysis and documentation to determine the quality of the spectrum. In this study, spectral signatures of pedunculate oak (Quercus robur L.) called Dedek and its clones were singled out. The objective of this research was to establish a foundation for a spectral library that would facilitate future research on the application of hyperspectral scanners for detecting protected tree species and their clones, all for the purpose of preservation of the genetic diversity of protected trees in Croatia. The findings of this research contribute to the conservation of Croatia's natural heritage by providing valuable insights into the genetic preservation of the 600-year-old "Dedek" Oak. Also, determining the spectral signatures of clones can help us with the identification of specific tree species (especially during aerial imaging, which is planned to be conducted in the future). In conclusion, this research showcases the importance of preserving historic trees and the potential of hyperspectral imaging as a valuable tool for genetic preservation efforts. The study's outcomes contribute to the long-term conservation of Croatia's natural heritage and provide a foundation for future research in the field of tree preservation and spectral library inception.

Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning.

Acquiring large amounts of hyperspectral data of small intestinal tissue with real labels in the clinic is difficult, and the data shows inter-patient variability. Building an automatic identification model using a small dataset presents a crucial challenge in obtaining a strong generalization of the model. This study aimed to explore the performance of hyperspectral imaging and transfer learning techniques in the automatic identification of normal and ischemic necrotic sites in small intestinal tissue. Hyperspectral data of small intestinal tissues were collected from eight white rabbit samples. The transfer component analysis (TCA) method was performed to transfer learning on hyperspectral data between different samples and the variability of data distribution between samples was reduced. The results showed that the TCA transfer learning method improved the accuracy of the classification model with less training data. This study provided a reliable method for single-sample modelling to detect necrotic sites in small intestinal tissue .