Search Results (706)

Seed phenomics is a critical research field for understanding seed germination mechanisms. Metasurfaces, composed of subwavelength nanostructures, offer a promising pathway to achieve both dispersion control and imaging functionalities within an ultra-compact form factor. Recent advances in micro–nano-optics and computational imaging have opened new avenues for high-dimensional, multimodal imaging. However, conventional hyperspectral and light-field systems still face limitations in compactness, depth resolution, and spectral–spatial integration. This review summarizes recent progress in metalens and metasurface lens array-based light-field systems for hyperspectral imaging and 3D reconstruction, with a focus on the underlying principles, design strategies, and reconstruction algorithms that enable single-shot 3D hyperspectral acquisition. We further present a forward-looking roadmap toward the realization of a revolutionized imaging paradigm: a metasurface-based light-field platform that fully integrates 3D and hyperspectral imaging capabilities. In particular, we examine how dispersive metasurfaces serve as core optical elements for precise dispersion control in hyperspectral imaging systems, while metalens arrays enable accurate modulation of spatial–angular distributions in light-field configurations. We systematically review both 3D and spectral reconstruction algorithms, highlighting their roles in decoding complex optical encodings. The application of these integrated systems in seed phenotyping is emphasized, demonstrating their capability to capture 3D spatial–spectral distributions in a single exposure. This approach facilitates high-throughput analysis of morphological traits, germination potential, and internal biochemical composition, offering a comprehensive solution for advanced seed characterization. Finally, we outline a practical roadmap for implementing a metasurface-based light-field platform that integrates hyperspectral imaging and computational 3D reconstruction. This review offers a comprehensive overview of the state of the art in compact 3D light-field systems and multimodal hyperspectral imaging platforms, while providing forward-looking insights aimed at advancing smart seed phenotyping, precision agriculture, and next-generation optical imaging technologies. Full article

Pak choi (Brassica chinensis L.) has a strong adsorption capacity for the heavy metal cadmium (Cd), which is a big threat to human health. Traditional detection methods have drawbacks such as destructiveness, time-consuming processes, and low efficiency. Therefore, this study aimed to construct a non-destructive prediction model for Cd content in pak choi leaves using hyperspectral technology combined with feature selection algorithms and multivariate regression models. Four different cadmium concentration treatments (0 (CK), 25, 50, and 100 mg/L) were established to monitor the apparent characteristics, chlorophyll content, cadmium content, chlorophyll fluorescence parameters, and spectral features of pak choi. Competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and random frog (RF) were used for feature wavelength selection. Partial least squares regression (PLSR), random forest regression (RFR), the Elman neural network, and bidirectional long short-term memory (BiLSTM) models were established using both full spectra and feature wavelengths. The results showed that high-concentration Cd (100 mg/L) significantly inhibited pak choi growth, leaf Cd content was significantly higher than that in the control group, chlorophyll content decreased by 16.6%, and damage to the PSII reaction centre was aggravated. Among the models, the FD–RF–BiLSTM model demonstrated the best prediction performance, with a determination coefficient of the prediction set (Rp²) of 0.913 and a root mean square error of the prediction set (RMSEP) of 0.032. This study revealed the physiological, ecological, and spectral response characteristics of pak choi under Cd stress. It is feasible to detect leaf Cd content in pak choi using hyperspectral imaging technology, and non-destructive, high-precision detection was achieved by combining chemometric methods. This provides an efficient technical means for the rapid screening of Cd pollution in vegetables and holds important practical significance for ensuring the quality and safety of agricultural products. Full article

Accurate malaria diagnosis is essential for effective case management and transmission control; however, the sensitivity, operational requirements, and field applicability of current conventional methods are limited. Hemozoin, an optically and magnetically active crystalline biomarker produced by Plasmodium species, offers a reagent-free target for next-generation diagnostics. This scoping review, following PRISMA-ScR and Joanna Briggs Institute guidance, synthesizes recent advances in hemozoin-based detection technologies and maps the current landscape. Twenty-four studies were reviewed, spanning eight major technology classes: magneto-optical platforms, magnetophoretic microdevices, photoacoustic detection, Raman/SERS spectroscopy, optical and hyperspectral imaging, NMR relaxometry, smartphone-based microscopy, and flow cytometry. Magneto-optical systems—including Hz-MOD, Gazelle™, and RMOD—demonstrated the highest operational readiness, with robust specificity but reduced sensitivity at low parasitemia. Photoacoustic Cytophone studies demonstrated promising sensitivity and noninvasive in vivo detection. Raman/SERS platforms achieved sub-100 infected cell/mL analytical sensitivity but remain laboratory-bound. Microfluidic and smartphone-based tools offer emerging, potentially low-cost alternatives. Across modalities, performance varied by parasite stage, with reduced detection of early ring forms. In conclusion, hemozoin-targeted diagnostics represent a rapidly evolving field with multiple viable translational pathways. While magneto-optical devices are closest to field deployment, further clinical validation, improved low-density detection, and standardized comparison across platforms are needed to support future adoption in malaria-endemic settings. Full article

As the global food industry expands and consumers demand higher food safety and quality standards, high-throughput detection technology utilizing digital intelligent optical sensors has emerged as a research hotspot in food testing due to its advantages of speed, precision, and non-destructive operation. Integrating cutting-edge achievements in optics, electronics, and computer science with machine learning algorithms, this technology efficiently processes massive datasets. This paper systematically summarizes the construction principles of intelligent optical sensors and their applications in food inspection. Sensors convert light signals into electrical signals using nanomaterials such as quantum dots, metal nanoparticles, and upconversion nanoparticles, and then employ machine learning algorithms including support vector machines, random forests, and convolutional neural networks for data analysis and model optimization. This enables efficient detection of target substances like pesticide residues, heavy metals, microorganisms, and food freshness. Furthermore, the integration of multiple detection mechanisms—including spectral analysis, fluorescence imaging, and hyperspectral imaging—has significantly broadened the sensors’ application scenarios. Looking ahead, optical sensors will evolve toward multifunctional integration, miniaturization, and intelligent operation. By leveraging cloud computing and IoT technologies, they will deliver innovative solutions for comprehensive monitoring of food quality and safety across the entire supply chain. Full article

Aimed at the problems of traditional wood species identification relying on manual experience, slow identification speed, and insufficient robustness, this study takes hyperspectral images of cross-sections of 10 typical wood species commonly found in Puer, Yunnan, China, as the research object. It comprehensively applies various spectral and texture feature extraction technologies and proposes an intelligent wood species identification method based on the fusion of multimodal texture-dominated features and deep learning. Firstly, an SOC710-VP hyperspectral imager is used to collect hyperspectral data under standard laboratory lighting conditions, and a hyperspectral database of wood cross-sections is constructed through reflectance calibration. Secondly, in the spectral space construction stage, a comprehensive similarity matrix is built based on four types of spectral similarity indicators. Representative bands are selected using two Max–Min strategies: partitioned quota and coverage awareness. Multi-scale wavelet fusion is performed to generate high-resolution fused images and extract interest point features. Thirdly, in the texture space construction stage, three types of texture feature matrices are generated based on the PCA first principal component map, and interest point features are extracted. Fourthly, in the complementary collaborative learning stage, the ST-former model is constructed. The weights of the trained SpectralFormer++ and TextureFormer are imported, and only the fusion weights are optimized and learned to realize category-adaptive spectral–texture feature fusion. Experimental results show that the overall classification accuracy of the proposed joint model reaches 90.27%, which is about 8% higher than that of single-modal models on average. Full article

Accurate colorimetric quantification presents a significant challenge, as traditional imaging technologies fail to resolve metamerism and even hyperspectral imaging (HSI) is compromised by nonlinearities and specular reflections. This study introduces a high-fidelity colorimetric system using cross-polarized HSI to suppress specular reflections, integrated with a Support Vector Regression (SVR) model to correct the system’s nonlinear response. The system’s performance was rigorously validated, demonstrating exceptional stability and repeatability (average $\Delta E_{00}<0.1$). The SVR calibration significantly enhanced accuracy, reducing the mean color error from $\Delta E_{00}=4.36$ to $0.43$. Furthermore, when coupled with a Random Forest classifier, the system achieved 99.0% accuracy in discriminating visually indistinguishable (metameric) samples. In application-specific validation, it successfully quantified cosmetic color shifts and achieved high-precision skin-tone matching with a fidelity as low as $\Delta E_{00}=0.82$. This study demonstrates that the proposed system, by synergistically combining cross-polarization and machine learning, constitutes a robust tool for high-precision colorimetry, addressing long-standing challenges and showing significant potential in fields like cosmetic science. Full article

Kiwifruit (Actinidia spp.) is valued for its sensory quality and nutritional richness but faces postharvest challenges such as rapid softening, chilling injury, and physiological disorders. Conventional management strategies help maintain quality yet insufficient to capture the complexity of ripening, stress physiology, and cultivar-specific variation. Recent research emphasizes the continuum from preharvest to postharvest, where orchard practices, harvest maturity, and handling conditions influence quality and storage potential. Omics-driven studies, particularly transcriptomics and metabolomics, have revealed molecular networks regulating softening, sugar–acid balance, pigmentation, antioxidant properties, and chilling tolerance. Integrated multi-omics approaches identify key biomarkers and gene–metabolite relationships linked to ripening and stress responses. Complementing omics, nondestructive estimation technologies, including hyperspectral imaging, near-infrared spectroscopy, acoustic profiling, and chemometric models are emerging as practical tools for real-time classification of maturity, quality, and storability. When calibrated with omics-derived biomarkers, these technologies provide predictive, non-invasive assessments that can be deployed across the supply chain. Together, the convergence of postharvest physiology, omics, and nondestructive sensing offers a pathway toward precision quality management and sustainable kiwifruit production. This review synthesizes recent advances across these domains, highlighting mechanistic insights, practical applications, and future directions for integrating omics-informed strategies with commercial postharvest technologies. Full article

The spectral characteristics of harmful insect pests in wheat fields were characterised using hyperspectral imaging for the first time. The analysis of spectral profiles revealed that reflectance is determined by the structure of the insect’s chitin and the colouration of its body surface. Insects with lighter or more vivid colours (white, yellow, or green) showed higher reflectance values compared to those with predominantly dark pigmentation. Reflectance was also influenced by the presence of wings, surface roughness, and the age of the insect. Each species exhibited distinct spectral patterns that allowed for differentiation not only from other insect species but also from the plant background. A classification model using PLS-DA was developed and demonstrated high accuracy in identifying 12 pest species, confirming the strong potential of hyperspectral imaging for species-level classification. The results validate the PLS-DA method for differentiating insects based on spectral characteristics and underscore the reliability of this approach for automated monitoring systems to detect phytophagous pests in crop fields. This technology could reduce insecticide use by 30–40% through targeted application. The research has both scientific and economic significance, laying the groundwork for integrating machine learning and computer vision into agricultural monitoring. It supports the advancement of precision farming and contributes to improved global food security. Full article

This article presents the results of a comprehensive study aimed at developing automated diagnostic methods for identifying spring wheat phytopathologies using hyperspectral imaging (HSI). The research aimed to create an effective plant disease detection system, including at the early stages, which is critically important for ensuring food security in regions where wheat plays a key role in the agro-industrial sector. The study analyses the spectral characteristics of major wheat diseases, including powdery mildew, fusarium head blight, septoria glume blotch, root rots, various types of leaf spots, brown rust, and loose smut. Healthy plants differ from diseased ones in that they show a mostly uniform tone without distinct spots or patches on hyperspectral images, and their spectra have a consistent shape without sharp fluctuations. In contrast, disease spectra, differ sharply from those of healthy areas and can take diverse forms. Wheat diseases with a light coating (powdery mildew, fusarium head blight) exhibit high reflectance; chlorosis in the early stages of diseases (rust, leaf spot, septoria leaf blotch) exhibits curves with medium reflectance, and diseases with dark colouration (loose smut, root rot) have low reflectance values. These differences in reflectance among fungal diseases are caused by pigments produced by the pathogens, which either strongly absorb light or reflect most of it. The presence or absence of pigment production is determined by adaptive mechanisms. Based on these patterns in the spectral characteristics and optical properties of the diseases, a classification model was developed with 94% overall accuracy. Random Forest proved to be the most effective method for the automated detection of wheat phytopathogens using hyperspectral data. The practical significance of this research lies in the potential integration of the developed phytopathology detection approach into precision agriculture systems and the use of UAV platforms, enabling rapid large-scale crop monitoring for the timely detection. The study’s results confirm the promising potential of combining hyperspectral technologies and machine learning methods for monitoring the phytosanitary condition of crops. Our findings contribute to the advancement of digital agriculture and are particularly valuable for the agro-industrial sector of Central Asia, where adopting precision farming technologies is a strategic priority given the climatic risks and export-oriented nature of grain production. Full article

Head and neck cancer (HNC) diagnostics are undergoing a transformative shift. Recent research published in Cancers highlights a paradigm shift in the comprehensive management of HNC, driven by precision oncology and disruptive technologies. AI-enhanced imaging and non-invasive biomolecular fingerprinting are redefining early detection, with tools like infrared spectroscopy and hyperspectral imaging delivering near-perfect accuracy and real-time surgical guidance. Liquid biopsy is emerging as a powerful surveillance modality, capable of detecting recurrence months before conventional imaging and offering prognostic insights via cell-free DNA analysis. Theranostic agents in nuclear medicine show promise for rare HNC subtypes, though broader molecular targets remain a challenge. These technologies may have utility for complex presentations such as proliferative verrucous leukoplakia (PVL)-associated oral squamous cell carcinoma (OSCC), which disproportionately affects women, and peri-implant OSCC, which is often misdiagnosed and requires aggressive intervention. Collectively, these innovations directly address long-standing challenges: early detection, accurate staging, treatment personalization, monitoring of minimal residual disease and timely cancer care—where diagnostics not only inform treatment but actively shape outcomes. This editorial underscores the urgency of integrating such tools into clinical pathways to improve survival and quality of life for HNC patients globally. Full article

Hyperspectral imaging (HSI) is a swiftly developing intraoperative and diagnostic technique in several clinical specialties. By monitoring oxygenation and biochemical markers, it helps with tissue viability, burn depth measurement, wound healing, and tumor detection. HSI facilitates real-time, harmless diagnosis throughout surgeries or outpatient settings, and allows for the detection of tumor boundaries with over 90% accuracy, according to clinical studies. Originally developed for remote sensing and aerospace applications, HSI has rapidly evolved and found increasing relevance across diverse sectors, including agriculture, environmental monitoring, food safety, pharmaceuticals, defense, and especially medical diagnostics. This review explores the origins, development, and expanding applications of HSI, with a particular emphasis on its role in healthcare. It discusses the operational principles and unique features of hyperspectral systems, such as their ability to produce spectral data cubes, perform non-destructive analysis, and integrate with emerging technologies like artificial intelligence and drone-based platforms. By comparing hyperspectral imaging to traditional and multispectral techniques, the review highlights its superior spectral resolution and versatility. Key challenges, including data volume, sensor calibration, and real-time processing, are also addressed. Finally, emerging trends such as miniaturization, integration with the Internet of Things, and sustainable system designs are examined, offering insights into the future directions and interdisciplinary potentials of HSI in both scientific research and practical applications. Full article

Chinese lacquer, a natural polymer with exceptional durability and cultural significance, has been widely used since the Warring States period. This review examines recent advances in lacquer identification techniques and their role in cultural heritage conservation. Drawing on five representative case studies—the B54 Japanese armor, Ba lacquerware from Lijiaba, a Qing Dynasty folding fan, Ryukyu lacquerware, and late Joseon objects—we show how integrated analytical approaches combining microscopy, spectroscopy, chromatography, and biochemical methods provide critical insights into composition, degradation, and conservation strategies. Key findings highlight (1) the effectiveness of multi-technique analysis in characterizing complex lacquer–metal interfaces and layered structures; (2) the recognition of regional and chronological variations in lacquer formulations, highlighting the need for standardized authentication protocols and shared databases; and (3) the promise of non-destructive technologies to reduce sampling and improve aging simulations. By critically synthesizing these case studies, the review highlights both methodological successes and persistent challenges, such as ethical constraints of sampling and limited understanding of long-term degradation. Ultimately, lacquer is positioned at the intersection of material science and cultural preservation, offering a transferable framework for global heritage protection. Future directions include hyperspectral imaging, bioinspired consolidants, and computational modeling to advance non-invasive diagnostics and sustainable conservation. Full article

Nuts such as pecans, almonds, peanuts, pistachios, and walnuts are nutrient-dense foods rich in unsaturated fatty acids and antioxidant compounds. Their regular consumption has been linked to significant health benefits, including reduced risks of cardiovascular disease, diabetes, and high cholesterol. With increasing global demand, ensuring the quality of nuts before they reach consumers is critical. Conventional quality assessment methods dominate the industry but are often subjective, destructive, time-intensive, environmentally burdensome, and laborious. Therefore, there is an urgent need for rapid, non-destructive, and objective alternatives capable of meeting modern quality standards. In this systematic review, we summarize traditional approaches for evaluating nut quality parameters and introduce hyperspectral imaging as a novel technique with promising applications. We examine its use in detecting nut adulteration, assessing chemical composition, identifying defects, and evaluating other quality traits. Limitations of hyperspectral imaging in industrial settings are also discussed, along with potential solutions and future directions. Given the relatively limited research area, approximately 44 relevant studies were critically reviewed. This work provides valuable insights for researchers and industry stakeholders developing innovative technologies for nut quality assessment. Full article

The global demand for high-quality rice necessitates advancements in milling technologies and quality assessment techniques that are rapid, accurate, and scalable. Traditional methods of rice evaluation are time-consuming and subjective, and are increasingly being replaced by artificial intelligence driven solutions that offer non-destructive, real-time monitoring capabilities. This review presents a comprehensive synthesis of current AI applications including machine vision, deep learning, spectroscopy, thermal imaging, and hyperspectral imaging for the assessment and classification of rice quality across various stages of processing. Major emphasis is put on the recent advances in convolutional neural networks (CNNs), YOLO architectures, and Mask R-CNN models, and their integration into industrial rice milling systems is discussed. Additionally, the review highlights next steps, notably designing lean AI architectures suitable for edge computing, hybrid imaging systems, and the creation of open-access datasets. Across recent rice-focused studies, classification accuracies for grading and varietal identification are typically ≥$90\%$ using machine vision and CNNs, while NIR–ANN models for physicochemical properties (e.g., moisture/protein proxies) commonly report strong fits ($R^2\approx 0.90\text{–}0.99$). End-to-end detectors/segmenters (e.g., YOLO/YO-LACTS) achieve high precision suitable for near real-time inspection. These results indicate that AI-based approaches can substantially outperform conventional evaluation in both accuracy and throughput. Full article

Tea red scab, a contagious disease affecting tea plants, can infect both buds and mature leaves. This study developed discrimination models to assess the severity of this disease using RGB and hyperspectral images. The models were constructed from a total of 1188 samples collected in May 2024. The results demonstrated that the model based on hyperspectral Imaging (HSI) data significantly outperformed the RGB-based model. Four spectral preprocessing methods were applied, among which the combination of SNV, SG, and FD (SNV-SG-FD) proved to be the most effective. To better capture long-range dependencies among spectral bands, a hybrid architecture integrating a Gated Recurrent Unit (GRU) with a one-dimensional convolutional neural network (1D-CNN), termed CNN-GRU, was proposed. This hybrid model was compared against standalone CNN and GRU benchmarks. The hyperparameters of the CNN-GRU model were optimized using the Newton-Raphson-based optimizer (NRBO) algorithm. The proposed NRBO-optimized SNV-SG-FD-CNN-GRU model achieved superior performance, with accuracy, precision, recall, and F₁-score reaching 92.94%, 92.54%, 92.42%, and 92.43%, respectively. Significant improvements were observed across all evaluation metrics compared to the single-model alternatives, confirming the effectiveness of both the hybrid architecture and the optimization strategy. Full article