Hyperspectral Research Articles

Hyperspectral imaging of acoustically trapped plastics

Hyperspectral imaging of acoustically trapped plastics

Study on the methods of hyperspectral image saliency detection based on MBCNN

Study on the methods of hyperspectral image saliency detection based on MBCNN

Postharvest Management Techniques for Improved Shelf Life of Horticultural Crops: A Review

Postharvest management in horticulture is important for maintaining the quality, safety, and shelf life of produce, significantly impacting global food security and economic stability. This review explores critical advancements in postharvest practices, focusing on quality control, safety standards, and emerging technologies. Postharvest quality assessment involves the evaluation of physical, chemical, and sensory attributes, supported by microbiological testing to ensure food safety. Global standards like HACCP, GlobalG.A.P., and ISO 22000 provide frameworks for ensuring compliance and market access. Technological innovations, including near-infrared spectroscopy, hyperspectral imaging, and smart sensors, enable precise, real-time monitoring of produce quality, enhancing operational efficiency. Emerging technologies are revolutionizing the field; nanotechnology offers advanced packaging solutions that improve shelf life and food safety, while drones and AI enhance postharvest monitoring and logistics through real-time data collection and predictive analytics. Blockchain technology introduces unprecedented levels of traceability and transparency, ensuring accountability and rapid response to food safety issues. The integration of these technologies not only improves the efficiency and sustainability of postharvest systems but also aligns with consumer demands for high-quality, safe, and sustainably sourced produce. This holistic approach reduces post- harvest losses, enhances market competitiveness, and contributes to global efforts in ensuring food security. The synergy of traditional quality control methods with cutting-edge technologies paves the way for a resilient and adaptive post- harvest management system that meets the evolving challenges of the agricultural sector.

A Polarization‐Insensitive and Adaptively‐Blazed Meta‐Grating Based on Dispersive Metasurfaces

AbstractThe diffraction efficiency of blaze gratings is optimized only at a specific frequency due to a fixed blaze angle, resulting in reduced and variable diffraction efficiencies over the working frequency band. Additionally, blazed gratings demonstrate polarization dependence due to their groove structures and the interaction of light with their surfaces. Consequently, designing gratings with constant diffraction efficiencies across a wide frequency bandwidth while maintaining polarization independence remains a challenge. Here, a design paradigm of dispersion engineerable meta‐grating inspired by orthogonal harmonic oscillations (OHO) is presented. Utilizing the OHO model, the phase dispersion of a metasurface can be precisely controlled, which applies to any unit cell featuring two orthogonal electromagnetic resonances. As a proof of concept, a polarization‐insensitive meta‐grating is showcased, where the blazed angle adapts with the incident frequency, ensuring broadband performance. In the experiment, the adaptively‐blazed grating measured an optimized and constant diffraction efficiency of ≈80% over the working wavelength range, i.e., 8.7–12.2 µm. The difference in diffraction efficiency between the two perpendicular linear polarization states remains within 4.6%. The proposed paradigm paves the way for meta‐device design based on precise dispersion engineering, which has potential applications in spectrometers, broadband beam forming and steering, hyperspectral imaging, etc.

Hyperspectral Imaging Combined with Deep Learning for the Early Detection of Strawberry Leaf Gray Mold Disease

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

Integrative hyperspectral, transcriptomic, and metabolomic analysis reveals the mechanism of tea plants in response to sooty mold disease.

Sooty mold (SM), caused by Cladosporium species, is a pervasive threat to tea plant health, affecting both canopy structure and crop yield. Despite its significance, understanding the complex interplay between defense genes and metabolites in tea plants across various SM-infected canopy layers remains limited. Our study employed hyperspectral imaging, transcriptomic profiling, and metabolomic analysis to decipher the intricate mechanisms underlying the tea plant's response to SM infection. Our hyperspectral imaging identified three critical wavelengths (552, 673, and 800nm) inflection points associated with varying degrees of SM infection. This non-invasive method allows for the precise assessment of disease progression. Concurrently, transcriptome analysis revealed a wealth of differentially expressed genes (DEGs) enriched in metabolic pathways, secondary metabolite biosynthesis, and plant-pathogen interactions. Cluster analysis highlighted an intensified immune response in A2 and A3 samples. A comprehensive metabolomic profile identified 733 co-changed metabolites in SM-infected leaves, with alcohols, lipids (free fatty acids), hydrocarbons, and amino acids significantly accumulating in A1, while flavonoids were predominantly upregulated in A2 and A3. Weighted Gene Co-Expression Network Analysis (WGCNA) uncovered five hub genes (Dormancy-associated protein, Serine/threonine-protein phosphatase, ABC transporter, and some uncharacterized proteins) and two hub metabolites (D-Mannitol and 17-Hydroxylinolenic Acid) that exhibit significant relationships with DEGs and metabolites. Further co-expression analysis indicated that tea plants mainly employed genes and metabolites related to the biosynthesis of secondary metabolites, plant hormone signal transduction, and plant-pathogen interaction to combat SM. This study establishes a foundation for understanding the immune mechanisms of tea plants across different canopy layers in response to SM infection. It not only sheds light on the complex defense strategies employed by tea plants but also identifies candidate genes and metabolites crucial for enhancing tea plant breeding and resistance to SM.

Nondestructive Detection of Litchi Stem Borers Using Multi-Sensor Data Fusion

To enhance lychee quality assessment and address inconsistencies in post-harvest pest detection, this study presents a multi-source fusion approach combining hyperspectral imaging, X-ray imaging, and visible/near-infrared (Vis/NIR) spectroscopy. Traditional single-sensor methods are limited in detecting pest damage, particularly in lychees with complex skins, as they often fail to capture both external and internal fruit characteristics. By integrating multiple sensors, our approach overcomes these limitations, offering a more accurate and robust detection system. Significant differences were observed between pest-free and infested lychees. Pest-free lychees exhibited higher hardness, soluble sugars (11% higher in flesh, 7% higher in peel), vitamin C (50% higher in flesh, 2% higher in peel), polyphenols, anthocyanins, and ORAC values (26%, 9%, and 14% higher, respectively). The Vis/NIR data processed with SG+SNV+CARS yielded a partial least squares regression (PLSR) model with an R2 of 0.82, an RMSE of 0.18, and accuracy of 89.22%. The hyperspectral model, using SG+MSC+SPA, achieved an R2 of 0.69, an RMSE of 0.23, and 81.74% accuracy, while the X-ray method with support vector regression (SVR) reached an R2 of 0.69, an RMSE of 0.22, and 76.25% accuracy. Through feature-level fusion, Recursive Feature Elimination with Cross-Validation (RFECV), and dimensionality reduction using PCA, we optimized hyperparameters and developed a Random Forest model. This model achieved 92.39% accuracy in pest detection, outperforming the individual methods by 3.17%, 10.25%, and 16.14%, respectively. The multi-source fusion approach also improved the overall accuracy by 4.79%, highlighting the critical role of sensor fusion in enhancing pest detection and supporting the development of automated non-destructive systems for lychee stem borer detection.

A dynamic neural network model for the identification of asbestos roofings in hyperspectral images covering a large regional area

AbstractAsbestos has been used extensively in several applications. Once it is known as a dangerous mineral, its usage has been prohibited and its identification and remediation play a very important role from the health safety point of view. Nowadays, deep learning techniques are used in many applications, especially for image analysis. They can be used to significantly reduce the time and cost of traditional detection methods. In this paper, taking advantage of asbestos spectral signature, a deep neural network is introduced in order to implement a complete methodology to identify asbestos roofings starting from hyperspectral images in a regional context. The novelty of the proposed approach is a dynamic mixing of models with different features, in order to accommodate classifications on widespread areas of both urban and rural territories. Indeed, the dataset used during the experiments described in this paper is a large one, consisting of many wide hyperspectral images with a geometric resolution of 1 m and with 186 bands, covering an entire region of approximately 8,000 . This is in contrast to other works in the literature where the analyzed areas are limited in size and uniform for physical features.

Nano-bio interactions of Gum Arabic-stabilized lanthanide-based upconverting nanoparticles: in vitro and in vivo study.

Lanthanide-based nanoparticles (Ln-NPs) are highly valued for their unique optical and magnetic properties, making them useful in various scientific fields, including materials science and biomedicine. This study investigated the use of Gum Arabic (GA), a natural, non-toxic biopolymer, as capping agent for Ln-NPs to enhance their biocompatibility and chemical and colloidal stability. Specifically, Er3+/Yb3+ co-doped NaGdF4 Ln-NPs were modified with GA, followed by their characterization with respect to upconversion properties and in vitro as well as in vivo toxicity. Herein, widely used ligand-free and polyacrylic acid (PAA)-capped Ln-NPs were used as reference materials. Importantly, the GA-modified Ln-NPs exhibited superior stability in aqueous and biologically relevant media, as well as relatively lower cytotoxicity across multiple cell lines, including U-87 MG, HEPG2, and J774A.1. In vivo studies using zebrafish embryos confirmed the minimal toxicity of GA-capped Ln-NPs. Despite overall low non-specific cellular uptake, hyperspectral imaging and inductively coupled plasma mass spectrometry confirmed the colocalization of the Ln-NPs as a function of their surface chemistry in both cell models and zebrafish. The results suggest GA as an effective surface-stabilizing agent for Ln-NPs, paving the way for future functionalization with targeting agents.

Hybrid convolutional network with enhanced graph attention mechanism (HCN-EGAM) for hyperspectral image classification

ABSTRACT Due to their reliance on superficial spectral or spatial information, current hyperspectral image classification techniques frequently fail to generate similarity measures that are accurate enough to create graph structures. We have developed a new end-to-end model, the hybrid convolutional network with enhanced graph attention mechanism (HCN-EGAM), to address this issue. It integrates an enhanced graph attention mechanism with 3D–2D hybrid convolution. In order to improve classification accuracy, this model synergistically uses the graph attention network module with the hybrid convolutional feature extraction module. The model starts by creating a 3D–2D hybrid convolutional network. This lets it quickly pull out deep spatial-spectral features that make different ground objects stand out in hyperspectral images. The model then employs these deep spatial-spectral properties to construct a graph structure, thereby enhancing its feature representation capabilities. Lastly, we use the Enhanced Graph Attention Mechanism (EGAM) to learn long-range spatial associations, successfully distinguishing between intra-class differences and inter-class similarities between distinct samples. Comparing our suggested method to other state-of-the-art methods, experimental assessments on three datasets, including the University of Pavia, Kennedy Space Center (KSC) and Indian Pines (IP) datasets, show a considerable improvement in classification accuracy.

Determining the optimal time window to detect emerald ash borer damage for effective management.

The emerald ash borer (EAB) is an invasive pest of global concern. Accurate detection of EAB is crucial for effective management. Traditional field surveys fail to meet large-scale monitoring requirements. Remote sensing methods offer a potential solution, but the phenological decline of ash trees may obscure the remote sensing features for detecting EAB. Therefore, determining the timing of leaf abscission caused by EAB before phenology is crucial for effective detection. We collected time-series data of Leaf Area Index (LAI), leaf sizes, and hyperspectral images of damaged ash trees throughout the growing season to determine the optimal detecting time window for EAB detection using field surveys or remote sensing techniques. Significant differences in LAI and leaf size were observed throughout the growing season among ash trees with different EAB infestation degrees, providing a basis for small-scale field surveys. However, in May and June, the hyperspectral reflectance showed no variation. The difference began to appear in July and became apparent from August to October. By October, severely EAB-infested ash trees had almost completely defoliated. Machine learning classification results showed that accuracies after July were higher than before July. After July, the highest classification accuracy reached 100%, while the highest accuracy before July was only 88.57%. Selecting the optimal monitoring time significantly enhanced detection accuracy. The optimal period for field surveys is from May to November, whereas for remote sensing it is from August to October. Identifying the optimal months enables us to achieve more efficient decision-making and management. © 2024 Society of Chemical Industry.

Hyperspectral imaging in neurosurgery: a review of systems, computational methods, and clinical applications.

Accurate identification between pathologic (e.g., tumors) and healthy brain tissue is a critical need in neurosurgery. However, conventional surgical adjuncts have significant limitations toward achieving this goal (e.g., image guidance based on pre-operative imaging becomes inaccurate up to 3cm as surgery proceeds). Hyperspectral imaging (HSI) has emerged as a potential powerful surgical adjunct to enable surgeons to accurately distinguish pathologic from normal tissues. We review HSI techniques in neurosurgery; categorize, explain, and summarize their technical and clinical details; and present some promising directions for future work. We performed a literature search on HSI methods in neurosurgery focusing on their hardware and implementation details; classification, estimation, and band selection methods; publicly available labeled and unlabeled data; image processing and augmented reality visualization systems; and clinical study conclusions. We present a detailed review of HSI results in neurosurgery with a discussion of over 25 imaging systems, 45 clinical studies, and 60 computational methods. We first provide a short overview of HSI and the main branches of neurosurgery. Then, we describe in detail the imaging systems, computational methods, and clinical results for HSI using reflectance or fluorescence. Clinical implementations of HSI yield promising results in estimating perfusion and mapping brain function, classifying tumors and healthy tissues (e.g., in fluorescence-guided tumor surgery, detecting infiltrating margins not visible with conventional systems), and detecting epileptogenic regions. Finally, we discuss the advantages and disadvantages of HSI approaches and interesting research directions as a means to encourage future development. We describe a number of HSI applications across every major branch of neurosurgery. We believe these results demonstrate the potential of HSI as a powerful neurosurgical adjunct as more work continues to enable rapid acquisition with smaller footprints, greater spectral and spatial resolutions, and improved detection.

Hyperspectral imaging analysis for early detection of tomato bacterial leaf spot disease.

Recent advancements in hyperspectral imaging (HSI) for early disease detection have shown promising results, yet there is a lack of validated high-resolution (spatial and spectral) HSI data representing the responses of plants at different stages of leaf disease progression. To address these gaps, we used bacterial leaf spot (Xanthomonas perforans) of tomato as a model system. Hyperspectral images of tomato leaves, validated against in planta pathogen populations for seven consecutive days, were analyzed to reveal differences between infected and healthy leaves. Machine learning models were trained using leaf-level full spectra data, leaf-level Vegetation index (VI) data, and pixel-level full spectra data at four disease progression stages. The results suggest that HSI can detect disease on tomato leaves at pre-symptomatic stages and differentiate bacterial disease spots from abiotic leaf spots. Using VI data as features for machine learning improved overall classification performance by 26-37% compared to the direct use of raw data. Critical wavelength bands and VIs varied across disease progression stages, suggesting that pre-symptomatic disease detection relied more on changes in leaf water content (1400nm) and plant defense hormone-mediated responses (750nm) rather than changes in leaf pigments or internal structure (800-900nm), which may become more crucial during symptomatic stages. In conclusion, this study provides valuable insights into the dynamics of bacterial spot disease, revealing the potential benefits of leaf structure segmentation and VI group pattern analysis in HSI studies for the early detection of leaf diseases.

Detection of the Pigment Distribution of Stacked Matcha During Processing Based on Hyperspectral Imaging Technology

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

Comparison of dimensionality reduction methods on hyperspectral images for the identification of heathlands and mires.

Hyperspectral data and machine learning offer great potential for identifying valuable open ecosystems. Due to the large volume of data, preprocessing of hyperspectral images must involve dimensionality reduction. The main goal of this study was to test the effectiveness of various types of feature reduction (feature selection and feature extraction) when performing classification using the Random Forest algorithm. A comparison was conducted between two ecosystems - heathlands and mires protected as Natura 2000 habitats. Two transformations of feature extraction were chosen, namely Minimum Noise Fraction (MNF) and Principal Component Analysis (PCA), while Linear Discriminant Analysis (LDA) was used as a feature selection method. It was proven that irrespective of the class type, accuracy is higher with the feature extraction method (mean F1 accuracy of 0.922) than with feature selection (mean F1 accuracy of 0.787). At the same time, no significant differences in accuracies were found between the MNF and PCA methods. Although LDA resulted in lower accuracies (0.816 for heathland and 0.750 for mires), the method could also be used due to relatively high F1 values. The effectiveness of the LDA method for feature reduction in open ecosystem identification was confirmed for the first time for open natural vegetation.

Detection of Rice Leaf Folder in Paddy Fields Based on Unmanned Aerial Vehicle-Based Hyperspectral Images

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

Bioinspired Passive Cooling Hydrogel for Visualizing Hygroscopicity and Desorption Process

AbstractSelf‐hygroscopic hydrogels, characterized by high evaporation enthalpy, cooling efficiency, and self‐regulating properties, have garnered significant attention. However, most current research focuses on enhancing the hygroscopic and desorption performance, often overlooking the importance of monitoring the self‐regulation process, which limits its further application. Advanced visualization technologies, such as in situ electrical impedance tomography, low‐field nuclear magnetic resonance, and hyperspectral imaging, offer potential insights into this behavior, yet they often require additional devices, incur high costs, and involve complex sample preparation processes. Therefore, drawing inspiration from nature, humidity‐color‐sensitive hydrogels (HCSHs) strategy is proposed for visualized cooling. Benefiting from the strong polar responsiveness of the aggregation‐induced emission (AIE) molecules, the hydrogel's fluorescence significantly changes with varying interior water content, thereby its self‐regulation process is monitored easily. Further, the obtained hydrogel could be applied in the electronic device cooling owing to the polymer skeletons’ high swelling ratio, strong adhesion, and excellent self‐hygroscopic properties. This strategy overcomes current limitations in the visual technology of self‐hygroscopic materials and provides new insights into intelligent thermal management for electronic devices.

Proto-DS: A Self-Supervised Learning-Based Nondestructive Testing Approach for Food Adulteration with Imbalanced Hyperspectral Data

Conventional food fraud detection using hyperspectral imaging (HSI) relies on the discriminative power of machine learning. However, these approaches often assume a balanced class distribution in an ideal laboratory environment, which is impractical in real-world scenarios with diverse label distributions. This results in suboptimal performance when less frequent classes are overshadowed by the majority class during training. Thus, the critical research challenge emerges of how to develop an effective classifier on a small-scale imbalanced dataset without significant bias from the dominant class. In this paper, we propose a novel nondestructive detection approach, which we call the Dice Loss Improved Self-Supervised Learning-Based Prototypical Network (Proto-DS), designed to address this imbalanced learning challenge. The proposed amalgamation mitigates the label bias on the most frequent class, further improving robustness. We validate our proposed method on three collected hyperspectral food image datasets with varying degrees of data imbalance: Citri Reticulatae Pericarpium (Chenpi), Chinese herbs, and coffee beans. Comparisons with state-of-the-art imbalanced learning techniques, including the Synthetic Minority Oversampling Technique (SMOTE) and class-importance reweighting, reveal our method’s superiority. Notably, our experiments demonstrate that Proto-DS consistently outperforms conventional approaches, achieving the best average balanced accuracy of 88.18% across various training sample sizes, whereas the Logistic Model Tree (LMT), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN) approaches attain only 59.42%, 60.38%, and 66.34%, respectively. Overall, self-supervised learning is key to improving imbalanced learning performance and outperforms related approaches, while both prototypical networks and the Dice loss can further enhance classification performance. Intriguingly, self-supervised learning can provide complementary information to existing imbalanced learning approaches. Combining these approaches may serve as a potential solution for building effective models with limited training data.

Advancing hyperspectral imaging techniques for root systems: a new pipeline for macro- and microscale image acquisition and classification.

Understanding the environmental impacts on root growth and root health is essential for effective agricultural and environmental management. Hyperspectral imaging (HSI) technology provides a non-destructive method for detailed analysis and monitoring of plant tissues and organ development, but unfortunately examples for its application to root systems and the root-soil interface are very scarce. There is also a notable lack of standardized guidelines for image acquisition and data analysis pipelines. This study investigated HSI techniques for analyzing rhizobox-grown root systems across various imaging configurations, from the macro- to micro-scale, using the imec VNIR SNAPSCAN camera. Focusing on three graminoid species with different root architectures allowed us to evaluate the influence of key image acquisition parameters and data processing techniques on the differentiation of root, soil, and root-soil interface/rhizosheath spectral signatures. We compared two image classification methods, Spectral Angle Mapper (SAM) and K-Means clustering, and two machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), to assess their efficiency in automating root system image classification. Our study demonstrated that training a RF model using SAM classifications, coupled with wavelength reduction using the second derivative spectra with Savitzky-Golay (SG) smoothing, provided reliable classification between root, soil, and the root-soil interface, achieving 88-91% accuracy across all configurations and scales. Although the root-soil interface was not clearly resolved, it helped to improve the distinction between root and soil classes. This approach effectively highlighted spectral differences resulting from the different configurations, image acquisition settings, and among the three species. Utilizing this classification method can facilitate the monitoring of root biomass and future work investigating root adaptations to harsh environmental conditions. Our study addressed the key challenges in HSI acquisition and data processing for root system analysis and lays the groundwork for further exploration of VNIR HSI application across various scales of root system studies. This work provides a full data analysis pipeline that can be utilized as an online Python-based tool for the semi-automated analysis of root-soil HSI data.

Deep learning-driven super-resolution in Raman hyperspectral imaging: Efficient high-resolution reconstruction from low-resolution data

Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. The results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs.