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Abstract Background Wheat (Triticum aestivum L.) is an important grain crops in the world, and its growth and development in different stages is seriously affected by saline-alkali stress, especially in seedling stage. Therefore, nondestructive detection of wheat seedlings under saline-alkali stress can provide more comprehensive technical support for wheat breeding, cultivation and management. Results This research focused on moisture signal prediction and classification of saline-alkali stress in wheat seedlings using fusion techniques. After collecting and analyzing transverse relaxation time and Multispectral imaging (MSI) information of wheat seedlings, four regression models were used to predict the moisture signal. K-Nearest Neighbor (KNN) and Gaussian-Naïve Bayes (GNB) models were combined with fivefold cross validation to classify the prediction of wheat seedling stress. The results showed that wheat seedlings would increase the bound water content through a certain mechanism to enhance their saline-alkali stress. Under the same Na concentration, the effect of alkali stress on moisture, growth and spectrum of wheat seedlings is stronger than salt stress. The Gradient Boosting Decision Regression Tree model performs the best in predicting wheat moisture signals, with a coefficient of determination (R2P) of 0.98 and a root mean square error of 109.60. It also had a short training time (1.48 s) and an efficient prediction speed (1300 obs/s). The KNN and GNB demonstrated significantly enhanced predictive performance when classifying the fused dataset, compared to using single datasets individually. In particular, the GNB model performing best on the fused dataset, with Precision, Recall, Accuracy, and F1-score of 90.30, 88.89%, 88.90%, and 0.90, respectively. Conclusions Under the same Na concentration, the effects of alkali stress on water content, spectrum, and growth of wheat were stronger than that of salt stress, which was more unfavorable to the growth of wheat. The fusion of low-field nuclear magnetic resonance and MSI technology can improve the classification of wheat stress, and provide an effective technical method for rapid and accurate monitoring of wheat seedlings under saline-alkali stress. Graphical Abstract

Abstract With rising melanoma incidence and mortality, early detection and surgical removal of primary lesions is essential. Multispectral imaging is a new, non-invasive technique that can facilitate skin cancer detection by measuring the reflectance spectra of biological tissues. Currently, incident illumination allows little light to be reflected from deeper skin layers due to high surface reflectance. A pilot study was conducted at the University Hospital Basel to evaluate, whether multispectral imaging with direct light coupling could extract more information from deeper skin layers for more accurate dignity classification of melanocytic lesions. 27 suspicious pigmented lesions from 23 patients were included (6 melanomas, 6 dysplastic nevi, 12 melanocytic nevi, 3 other). Lesions were imaged before excision using a prototype snapshot mosaic multispectral camera with incident and direct illumination with subsequent dignity classification by a pre-trained multispectral image analysis model. Using incident light, a sensitivity of 83.3% and a specificity of 58.8% were achieved compared to dignity as determined by histopathological examination. Direct light coupling resulted in a superior sensitivity of 100% and specificity of 82.4%. Convolutional neural network classification of corresponding red, green, and blue lesion images resulted in 16.7% lower sensitivity (83.3%, 5/6 malignant lesions detected) and 20.9% lower specificity (61.5%) compared to direct light coupling with multispectral image classification. Our results show that incorporating direct light multispectral imaging into the melanoma detection process could potentially increase the accuracy of dignity classification. This newly evaluated illumination method could improve multispectral applications in skin cancer detection. Further larger studies are needed to validate the camera prototype.

Abstract Vegetation density extraction is influenced by the characteristics of satellite images, vegetation type, classification algorithm, and region, but there is little information about multispectral imaging (MSI). Studying the compatibility of the information content of sensors to replace sensors in areas where there is no easy access to their data is necessary for remote sensing (RS) studies. This study aims to assess the suitability of MSI from Sentinel-2 and Spot-5 satellites for generating pasture density maps. The Middle Kashkan watershed in the Lorestan Province of Iran was the study area. Geometric correction of the images was performed using ground control points (GCP) and the area's digital elevation model, achieving an accuracy of 0.21 pixels or better. Supervised classification techniques including parallelogram, minimal distance, maximum likelihood, and artificial neural network (ANN) algorithms were applied to the primary MSI of each satellite, as well as the integrated image of Spot-5 and the resulting pasture density map. Three density categories were considered: 5–25%, 25–50%, and over 50%. To validate the accuracy of the classification, a ground truth map of the region was created by interpreting a referenced official digital orthophotomosaic image at a scale of 1:40,000. Comparative analysis of the classified images revealed that the Sentinel-2 image with PCA-2-8 band composition and ANN classification algorithm yielded superior results, with an overall accuracy of 65.72% and a kappa coefficient of 0.08, compared to the Spot-5 image with PCA-3-1 band composition and the ANN classification algorithm. Spot-5 satellite images demonstrated greater effectiveness in generating pasture cover maps across the three density categories. These findings suggest that satellite images with suitable spatial and spectral resolution can be effectively utilized for generating accurate pasture density maps and monitoring long-term pasture preservation, particularly in regions characterized by high aerial photography altitudes in pasture areas. This approach holds the potential for effective pasture management and conservation efforts on a global scale.

Abstract Background Pepper Phytophthora blight is a devastating disease during the growth process of peppers, significantly affecting their yield and quality. Accurate, rapid, and non-destructive early detection of pepper Phytophthora blight is of great importance for pepper production management. This study investigated the possibility of using multispectral imaging combined with machine learning to detect Phytophthora blight in peppers. Peppers were divided into two groups: one group was inoculated with Phytophthora blight, and the other was left untreated as a control. Multispectral images were collected at 0-h samples before inoculation and at 48, 60, 72, and 84 h after inoculation. The supporting software of the multispectral imaging system was used to extract spectral features from 19 wavelengths, and textural features were extracted using a gray-level co-occurrence matrix (GLCM) and a local binary pattern (LBP). The principal component analysis (PCA), successive projection algorithm (SPA), and genetic algorithm (GA) were used for feature selection from the extracted spectral and textural features. Two classification models were established based on effective single spectral features and significant spectral textural fusion features: a partial least squares discriminant analysis (PLS_DA) and one-dimensional convolutional neural network (1D-CNN). A two-dimensional convolutional neural network (2D-CNN) was constructed based on five principal component (PC) coefficients extracted from the spectral data using PCA, weighted, and summed with 19-channel multispectral images to create new PC images. Results The results indicated that the models using PCA for feature selection exhibit relatively stable classification performance. The accuracy of PLS-DA and 1D-CNN based on single spectral features is 82.6% and 83.3%, respectively, at the 48h mark. In contrast, the accuracy of PLS-DA and 1D-CNN based on spectral texture fusion reached 85.9% and 91.3%, respectively, at the same 48h mark. The accuracy of the 2D-CNN based on 5 PC images is 82%. Conclusions The research indicates that Phytophthora blight infection can be detected 48 h after inoculation (36 h before visible symptoms). This study provides an effective method for the early detection of Phytophthora blight in peppers.

Traits that rapidly respond to stress in important agricultural crops have the potential to provide growers with actionable feedback. Traits that respond to water-restriction could inform irrigation systems by identifying crop water status and requirements in real-time. This would be particularly useful for potato, which is extremely susceptible to drought. We conducted two pot experiments and one field experiment to evaluate the utility of two traits, canopy temperature and leaf greenness, for informing irrigation management in potatoes. We also evaluated the efficacy of Phenospex PlantEye F500 sensors for the remote sensing of leaf greenness. We found that canopy temperatures of the cvs. Maris Piper (Spring Pot Experiment, + 0.8 °C; Autumn Pot Experiment, + 5.3 °C) and Désirée (Autumn Pot Experiment, + 2.5 °C) increased with water-restriction and that the canopy temperatures of Maris Piper returned to its baseline within three days after the resumption of well-watered conditions. We also found that these responses varied between cultivars, with predictable outcomes based on reported and corroborated drought tolerance ratings. Leaf greenness was not affected by water restriction in the Spring pot experiment but had a significant interaction with sampling date and water restriction in the Autumn pot experiment. However, leaf greenness measurements from the Phenospex PlantEye F500 were significantly correlated with SPAD values, suggesting this tool might be useful in the screening for drought-tolerant cultivars in the future.

Monitoring food quality throughout the supply chain in a rapid and cost-effective way allows on-time decision−making, reducing food waste, and increasing sustainability. A portable multispectral imaging sensor was used for the rapid prediction of microbiological quality of fish fillets. Seabream fillets, packaged either in aerobic or vacuum conditions, were collected from both aquaculture and retail stores, while images were also acquired both from the skin and the flesh side of the fish fillets. In parallel to image acquisition, the microbial quality was also estimated for each fish fillet. The data were used for the training of predictive artificial neural network (ANN) models for the estimation of total aerobic counts (TACs). Models were built separately for fish parts (i.e., skin, flesh) and packaging conditions and were validated using two approaches (i.e., validation with data partitioning and external validation using samples from retail stores). The performance of the ANN models for the validation set with data partitioning was similar for the data collected from the flesh (RMSE = 0.402–0.547) and the skin side (RMSE = 0.500–0.533) of the fish fillets. Similar performance also was obtained from validation of the models of the different packaging conditions (i.e., aerobic, vacuum). The prediction capability of the models combining both air and vacuum packaged samples (RMSE = 0.531) was slightly lower compared to the models trained and validated per packaging condition, individually (RMSE = 0.510, 0.516 in air and vacuum, respectively). The models tested with unknown samples (i.e., fish fillets from retail stores-external validation) showed poorer performance (RMSE = 1.061–1.414) compared to the models validated with data partitioning (RMSE = 0.402–0.547). Multispectral imaging sensor appeared to be efficient for the rapid assessment of the microbiological quality of fish fillets for all the different cases evaluated. Hence, these outcomes could be beneficial not only for the industry and food operators but also for the authorities and consumers.

Non-invasive potato defects detection has been demanded for sorting and grading purpose. Researches on the classification of the defects has been available, however, investigation on the severity level calculation is limited. For the detection of the common scab, it has been found that imaging in the infrared region provide an interesting characteristic that could distinguish defected area to normal area. Thus, investigations on this wavelength range is interesting to add more knowledge and for applications. In this research, the multispectral image has been obtained and investigated especially at three wavelengths (950, 1 150, 1 600 nm). Image pre-processing and pseudo-color conversion techniques were explored to enhance the contrast between defects, normal background skin area and soil deposits. Results show that external defects, such as common scab and some mechanical damage types, appear brighter in the near infrared region, especially at 1 600 nm against the normal skin background. It has been found that pseudo-color images conversion provides more information regarding type if surface characteristics compared to grayscale single imaging. Image segmentation using pseudo-color images after multiplication operation pre-processing could be used for common scab and mechanical damage detection excluding soil deposits with a Dice Sorensen coefficient of 0.64. In addition, image segmentation using single image at 1 600 nm shown relatively better results with Dice Sorensen coefficient of 0.72 with note that thick soil deposits will also be segmented. Defect severity level evaluation had an R2 correlation of 0.84 against standard measurements of severity.

The assessment of plant health and quality is a critical aspect of plant biology, agriculture, and the food industry. With the global population continuously increasing, the demand for high-quality plant products is expected to surge. Consequently, there is a necessity for the development of automated systems that are user-friendly, accurate, affordable, and capable of rapid evaluation of plant health in a multitude of settings, including fields, farms, and laboratories. Such systems would apply to various applications, including investigating the impact of environmental conditions or developing new potential biostimulants and biopesticides. This paper introduces a novel, low-cost, and innovative multifunctional high-throughput plant phenotyping system that addresses an unmet need in the market. The system was designed to be affordable, scalable, applicable, and reliable to diverse customers. The key components of the system include a robotic arm AR4 (Annin Robotics, USA), a three-dimensional (3D) scanner POP 3 (Revopoint, USA), and a multispectral (MS) visible near-infrared (VNIR) camera FS 3200D 10GE (JAI Ltd., Japan). The paper describes these devices, their calibration, performance evaluation, and final applicability assessment. In particular, the motion characteristics of the AR4 are evaluated by a pose repeatability with obtained values of Image 1 and Image 2 without load and with load, respectively. Furthermore, it is shown that the calibrated camera provides comparable NDVI index data with the content of plant pigments (R2>0.92) and also against the reference VNIR hyperspectral (HS) camera SPECIM PFD4K-65-V10E (R2>0.99). In addition, the presented 3D scanner demonstrated superior 3D models with a high degree of fit compared to the more expensive 3D scanners such as Shining 3D EinScan Pro 2X 2020 or Shining 3D EinScan-SP V2. The paper concludes with a discussion of the results, limitations, future improvements, and potential applications of the device in laboratory, educational, and field settings.

Nitrogen is the most important nutritional element during the vegetative growth phase of the pineapple crop; however, its presence in the soil is insufficient to meet plant demands. In this study, nine machine learning techniques were validated to estimate the total nitrogen (TN) content in MD2 pineapple crops from data from multiple sources. These sources included multispectral images captured by an unmanned aerial vehicle (UAV); in situ sensors, which collected information on ecological factors such as pH, temperature, solar radiation, relative humidity, soil moisture, wind speed and direction, as well as SPAD values indicating leaf chlorophyll content.Total nitrogen (TN) values were taken from leaf tissue samples, which were then analyzed in a laboratory. To introduce nitrogen variability, a complete randomized block experimental design was implemented, applying five different treatments in five blocks, each with 12 replications, during a 6-month period in a pineapple crop located in Tauramena, Colombia. To address the inherent variability in the agricultural and environmental data, dimensionality was reduced using Principal Component Analysis (PCA). In addition, regularization techniques were applied, including cross-validation, feature selection, boost methods, L1 (Lasso) and L2 (Ridge) regularization, as well as hyperparameter optimization. These strategies generated more robust and accurate models, with the multilayer perceptron regressor (MLP regressor) and extreme gradient boosting (XGBoost) algorithms standing out. On the first sampling date, XGBoost achieved an R2 of 86.98 %, being the highest. On the following dates, MLP achieved a R2 of 59.11 % on the second date; XGBoost achieved a R2 of 68.00 % on the third date, and on the last date, MLP achieved a R2 of 69.4 %. These results indicate that the integration of data from multiple sources and the use of machine learning models could greatly improve the precision of nitro-gen (N) diagnostics in pineapple crops, especially in real-time applications. These findings highlight the promising potential of developing machine learning models that integrate multisensor data fusion for various applications in agriculture.

Multispectral Imaging has been used in many fields. In the medical field, Multispectral Imaging is still in its infancy. However, due to its excellent potential, it will also become one of the most important medical imaging in the future. This paper is the first bibliometric study in this field. The study comprehensively searched all relevant documents in Web of Science Core Collection from Jan 1, 1999 to Dec 31, 2022, systematically sorted out the author, journal, country and institution in this field, and analyzed the keywords. Based on this, the study suggests that researchers and healthcare workers should strengthen cooperation to apply Multispectral Imaging to more medical fields while further developing related technologies. At the same time, in the future, this field should focus on non-ex vivo tissue detection and the combination of Multispectral Imaging and artificial intelligence.

Introduction: Melanocytic nevi (MN), warts, seborrheic keratoses (SK), and psoriasis are four common types of skin surface lesions that typically require dermatoscopic examination for definitive diagnosis in clinical dermatology settings. This process is labor-intensive and resource-consuming. Traditional methods for diagnosing skin lesions rely heavily on the subjective judgment of dermatologists, leading to issues in diagnostic accuracy and prolonged detection times. Objectives: This study aims to introduce a multispectral imaging (MSI)-based method for the early screening and detection of skin surface lesions. By capturing image data at multiple wavelengths, MSI can detect subtle spectral variations in tissues, significantly enhancing the differentiation of various skin conditions. Methods: The proposed method utilizes a pixel-level mosaic imaging spectrometer to capture multispectral images of lesions, followed by reflectance calibration and standardization. Regions of interest were manually extracted, and the spectral data were subsequently exported for analysis. An improved one-dimensional convolutional neural network is then employed to train and classify the data. Results: The new method achieves an accuracy of 96.82 % on the test set, demonstrating its efficacy. Conclusion: This multispectral imaging approach provides a non-contact and non-invasive method for early screening, effectively addressing the subjective identification of lesions by dermatologists and the prolonged detection times associated with conventional methods. It offers enhanced diagnostic accuracy for a variety of skin lesions, suggesting new avenues for dermatological diagnostics.

Chlorophyll monitoring is an important topic in phenotypic research. For fruit trees, chlorophyll content can reflect the real-time photosynthetic capacity, which is a great reference for nutrient status assessment. Traditional in situ estimation methods are labor- and time-consuming. Remote sensing spectral imagery has been widely applied in agricultural research. This study aims to explore a transferable model to estimate canopy SPAD across growth stages and tree species. Unmanned aerial vehicle (UAV) system was applied for multispectral images acquisition. The results showed that the univariate model yielded with Green Normalized Difference Vegetation Index (GNDVI) gave valuable prediction results, providing a simple and effective method for chlorophyll monitoring for single species. Reflection features (RF) and texture features (TF) were extracted for multivariate modeling. Gaussian Process Regression (GPR) models yielded better performance for mixed species research than other algorithm models, and the R2 of the RF+TF+GPR model was approximately 0.7 in both single and mixed species. In addition, this method can also be used to predict canopy SPAD over various growth stages, especially in the third and fourth stages with R2 higher than 0.6. This paper highlights the importance of using RF+TF for canopy feature expression and deep connection exploration between canopy features with GPR algorithm. This research provides a universal model for canopy SPAD inversion which can promote the growth status monitoring and management of fruit trees.

Botrytis cinerea is an airborne plant pathogen that causes grey mould disease on horticultural crops, such as tomato, cucumber, pepper, and more. Early detection is crucial for timely and effective strategies for crop loss prevention. In this study, in planta assays were set up enabling a thorough assessment of grey mould progress on cucumber plants both artificially inoculated with two different inoculation methods, and naturally infected throughout the whole growing season of the host under controlled conditions simulating the environment typically prevailing in greenhouses. Multi-spectral imaging was used to capture a novel dataset across various spectral bands containing multiple stages of infection. Deep learning (DL) segmentation architectures, combined with Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) encoders, were employed to identify B. cinerea at various infection stages. The U-Net++ model with a MobileViT-S encoder performed best, achieving a Dice Coefficient (DC) of 0.677, an Intersection over Union (IoU) of 0.656, and a recall rate of 0.807, with a high overall accuracy of 90.1 %. In regards to early detection, the model achieves an IoU of 0.375 on 2nd day post inoculation (dpi), 0.230 on 1st dpi and 0.437 on the 6th dpi. The infection stages were compared to similar visible symptoms of physiological decline or plant responses to abiotic stress. The outcomes of this study demonstrate an important advance in the Artificial Intelligence (AI) plant health detection, for early crop disease diagnosis.

To maximise the throughput of novel, high-throughput phenotyping platforms, many researchers have utilised smaller pot sizes to increase the number of biological replicates that can be grown in spatially limited controlled environments. This may confound plant development through a process known as “pot binding”, particularly in larger species including potato (Solanum tuberosum), and under water-restricted conditions. We aimed to investigate the water availability hypothesis of pot binding, which predicts that small pots have insufficient water holding capacities to prevent drought stress between irrigation periods, in potato. Two cultivars of potato were grown in small (5 L) and large (20 L) pots, were kept under polytunnel conditions, and were subjected to three irrigation frequencies: every other day, daily, and twice daily. Plants were phenotyped with two Phenospex PlantEye F500s and canopy and tuber fresh mass and dry matter were measured. Increasing irrigation frequency from every other day to daily was associated with a significant increase in fresh tuber yield, but only in large pots. This suggests a similar level of drought stress occurred between these treatments in the small pots, supporting the water availability hypothesis of pot binding. Further increasing irrigation frequency to twice daily was still not sufficient to increase yields in small pots but it caused an insignificant increase in yield in the larger pots, suggesting some pot binding may be occurring in large pots under daily irrigation. Canopy temperatures were significantly higher under each irrigation frequency in the small pots compared to large pots, which strongly supports the water availability hypothesis as higher canopy temperatures are a reliable indicator of drought stress in potato. Digital phenotyping was found to be less accurate for larger plants, probably due to a higher degree of self-shading. The research demonstrates the need to define the optimum pot size and irrigation protocols required to completely prevent pot binding and ensure drought treatments are not inadvertently applied to control plants.

Plasmonic metamaterial absorbers (PMAs) designed for multispectral imaging in the infrared (IR) with uncooled microbolometers are investigated. The study presents Fourier transform infrared spectroscopy (FTIR) measurements of PMAs consisting of metal-insulator-metal-stacks (MIM) with square-shaped micropatches as top metal layers. The measurements reveal high absorptances of 82% to 99% for distinct wavelengths within a range from 2 μm to 9.2 μm. The spectra are evaluated with respect to the lateral dimensions of the patches and to the refractive indices of the used dielectrics SiO2, Al2O3 and Ta2O5. Numerical simulations and analytical calculations of the TM010-mode using the transmission line model (TLM) for microstrip antennas show good qualitative agreement with the measurement results. Additionally, bispectral PMAs were fabricated consisting of fields of PMAs with two different patch sizes arranged in a chessboard pattern. The individual fields of this pattern correspond to microbolometers with 12 μm pitch in shape and size. Two distinct absorption maxima can be seen in the spectra measured by FTIR. The choice of materials, deposition methods and patterning processes is suitable for the integration into the existing Fraunhofer IMS's nanotube microbolometer technology to realize multispectral infrared imaging. The fabrication process is CMOS-compatible and carried out on 8-in. wafers.

The digital restoration of historical manuscripts centers on deciphering hidden writings, made imperceptible to the naked eye due to factors such as erasure, fading, carbonization, and aging effects. Recent advancements in modern technologies have significantly improved our ability to unveil and interpret such written cultural heritage that, for centuries, had remained inaccessible to contemporary understanding. This paper aims to present a critical overview of state-of-the-art technologies, engaging in discussions about perspectives and limitations, and anticipating future applications. Serving as a practical guide, this work seeks to assist in the selection of techniques for digitally restoring ancient writings. Additionally, potential and challenges associated with integrating these techniques with advanced machine-learning approaches are also outlined.

Abstract Testing fundamental theories of diversity and evolutionary change often requires tracking the frequencies of clonal lineages within a population over time. Current tracking methods in plankton and microbial systems are often labour‐intensive, time‐consuming, expensive and/or unavailable, especially when high temporal resolution of frequencies is required. The combination of multispectral imaging flow cytometry and neural networks (NN) could provide an efficient approach to classify clonal lineages by their heritable phenotypes and thus track their frequency changes. Here, we present a novel method that combines NN and feature values extracted from images to classify six clonal lineages of the green alga Chlamydomonas reinhardtii based on heritable morphological differences when paired in‐silico and in‐vitro. We compared different NN trained on all six clonal lineages or on pairs of two and compared the accuracy of the models. All NN were able to classify clonal lineages with very high accuracy and the NN trained on pairs of clonal lineages achieved the highest accuracy of 97%. Using in‐vitro samples we observed a drop in accuracy to an average of 85%, which was due to the variation occurring between image acquisitions and culturing conditions causing differences in the extracted feature values. This method can be applied to reduce the workload and running costs of long‐term lineage tracking, and it can be applied to a wide range of plankton and microbial organisms and research questions.

Analyzing time series from remote sensing data can aid in understanding spectral-temporal phenomena in ecosystems, such as the seasonal variation of plant components. Lately, deep learning has emerged as a strong method for mapping environmental variables from this data due to its exceptional predictive capabilities. This work studies the adaptation of the ResNet computer vision architecture for time series analysis of Sentinel-2 data. The resulting deep learning architecture, ResNeTS, stacks sequential convolutions to build a deep and narrow network, aligning with the design principles of leading convolutional architectures in computer vision. Experiments were carried out for predicting different plant-biodiversity indices, namely, species richness, and Shannon and Simpson indices, for temperate grassland ecosystems. The results show that ResNeTS can achieve moderate improvements in terms of accuracy compared to other state-of-the-art architectures, such as InceptionTime (up to +0.021 $r^{2}$), with reduced computational costs owing to its streamlined architecture.

Stand-level forest tree species perception and identification are needed for monitoring-related operations, being crucial for better biodiversity and inventory management in forested areas. This paper contributes to this knowledge domain by researching tree trunk types multispectral perception at stand-level. YOLOv5 and YOLOv8 - Convolutional Neural Networks specialized at object detection and segmentation - were trained to detect and segment two tree trunk genus (pine and eucalyptus) using datasets collected in a forest region in Portugal. The dataset comprises only two categories, which correspond to the two tree genus. The datasets were manually annotated for object detection and segmentation with RGB and RGB-NIR images, and are publicly available. The “Small” variant of YOLOv8 was the best model at detection and segmentation tasks, achieving an F1 measure above 87% and 62%, respectively. The findings of this study suggest that the use of extended spectra, including Visible and Near Infrared, produces superior results. The trained models can be integrated into forest tractors and robots to monitor forest genus across different spectra. This can assist forest managers in controlling their forest stands.

Abstract Quantifying how colours stimulate animal eyes is an essential first step in many areas of biology, from behavioural and visual ecology to neuroethology. Although multiple methods exist, their relative accuracies remain untested, leaving researchers unsure which methods are most accurate and which might be unacceptably inaccurate. Here, I compare measurements from reflectance spectrometry and four camera‐based techniques to ground‐truth spectroradiometric measurements of radiance, using eight diverse visual systems. Because errors may compound through successive calculations, I calculate not only relative quantum catches but also chroma and colour contrasts. I find that filters mimicking photoreceptor spectral sensitivity curves, through either custom fabrication or weighted additive combinations of bandpass filters (‘computational filters’), as well as reflectance spectrometry, are the most accurate techniques, with most R2 > 0.98. Statistical filters trained on the library of reflectance spectra in the multispectral image calibration and analysis (MICA) toolbox lag behind in accuracy, particularly for dark colours, with R2 values dipping as low as 0.23. However, the performance of these filters is vastly improved (all R2 > 0.96) if one (a) uses the full UV–VIS range of camera and animal vision, even for animals lacking UV photoreceptors and (b) expands the library of training spectra to include simulated low‐reflectance spectra. Statistical filters trained on either a 24‐ or 45‐colour chart (an alternative method offered by the MICA toolbox) vary wildly in accuracy, with R2 values ranging from 0.11 to 0.94, and should not be trusted. For those requiring point measurements only, reflectance spectrometry is highly accurate within the constraints described in the main text. Those using photography and desiring the highest levels of accuracy should opt for custom‐fabricated or computational filters. Statistical filters trained on the MICA toolbox's library of natural spectra can be expected to be reasonably accurate for many combinations of visual systems and stimuli, but only if (a) the full UV–VIS spectrum is used and (b) the training library is expanded to include simulated low‐reflectance spectra. Statistical filters trained on a colour chart are highly unreliable and their use should be discontinued.

Objective: Blood circulation is an important indicator of wound healing. In this study, a tissue oxygen saturation detecting (TOSD) system that is based on multispectral imaging (MSI) is proposed to quantify the degree of tissue oxygen saturation (StO2) in cutaneous tissues. Methods: A wound segmentation algorithm is used to segment automatically wound and skin areas, eliminating the need for manual labeling and applying adaptive tissue optics. Animal experiments were conducted on six mice in which they were observed seven times, once every two days. The TOSD system illuminated cutaneous tissues with two wavelengths of light - red ( $\mathrm {\lambda } = 660$ nm) and near-infrared ( $\mathrm {\lambda } = 880$ nm), and StO2 levels were calculated using images that were captured using a monochrome camera. The wound segmentation algorithm using ResNet34-based U-Net was integrated with computer vision techniques to improve its performance. Results: Animal experiments revealed that the wound segmentation algorithm achieved a Dice score of 93.49%. The StO2 levels that were determined using the TOSD system varied significantly among the phases of wound healing. Changes in StO2 levels were detected before laser speckle contrast imaging (LSCI) detected changes in blood flux. Moreover, statistical features that were extracted from the TOSD system and LSCI were utilized in principal component analysis (PCA) to visualize different wound healing phases. The average silhouette coefficients of the TOSD system with segmentation (ResNet34-based U-Net) and LSCI were 0.2890 and 0.0194, respectively. Conclusion: By detecting the StO2 levels of cutaneous tissues using the TOSD system with segmentation, the phases of wound healing were accurately distinguished. This method can support medical personnel in conducting precise wound assessments. Clinical and Translational Impact Statement—This study supports efforts in monitoring StO2 levels, wound segmentation, and wound healing phase classification to improve the efficiency and accuracy of preclinical research in the field.

Noninvasive methods for assessing in-vivo skin blood perfusion parameters, such as hemoglobin oxygenation, are crucial for diagnosing and monitoring microvascular diseases. This approach is particularly beneficial for patients with compromised skin, where standard contact-based clinical devices are inappropriate. For this goal, we propose the analysis of multimodal data from an occlusion protocol applied to 18 healthy participants, which includes multispectral imaging of the whole hand and reference photoplethysmography information from the thumb. Multispectral data analysis was conducted using two different blind linear unmixing methods: principal component analysis (PCA), and extended blind endmember and abundance extraction (EBEAE). Perfusion maps for oxygenated and deoxygenated hemoglobin changes in the hand were generated using linear multivariable regression models based on the unmixing methods. Our results showed high accuracy, with $\text {R}^{2}$-adjusted values, up to 0.90 $\pm$ 0.08. Further analysis revealed that using more than four characteristic components during spectral unmixing did not improve the fit of the model. Bhattacharyya distance results showed that the fitted models with EBEAE were more sensitive to hemoglobin changes during occlusion stages, up to four times higher than PCA. Our study concludes that multispectral imaging with EBEAE is effective in quantifying changes in oxygenated hemoglobin levels, especially when using 3 to 4 characteristic components. Our proposed method holds promise for the noninvasive diagnosis and monitoring of superficial microvascular alterations across extensive anatomical regions.

This study aimed at measuring beef marbling scores in an objective and simple manner through spectral imaging and multiple linear regression (MLR). Beef marbling prediction by hyperspectral imaging and partial least squares regression (PLSR) was analyzed to calibrate and evaluate an MLR model with a few selected wavelengths. Data came from 44 beef samples and consisted of their spectral signatures (75 wavelengths) from hyperspectral reflectance images (400-1000 nm) and their marbling scores assigned by evaluators. The wavelengths that presented regression coefficients with the highest absolute values in the PLSR model, were used to calibrate the MLR model by a backward stepwise approach (p < 0.05). The coefficient of determination for prediction (R2p) and the standard error of prediction (SEP) were evaluated. The MLR model was suitable for practical use because it required only 12 wavelengths for reliable predictions (R2p = 0.824 > 0.8; SEP = 11.4% < 15%). A model is proposed for the objective and simple measurement of beef marbling score using multispectral imaging technology.

Diseases caused by invasive pathogens are an increasing threat to forest health, and early and accurate disease detection is essential for timely and precision forest management. The recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and trees. In this study, Dutch elm disease (DED; caused by Ophiostoma novo-ulmi,) and American elm (Ulmus americana) was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper- and multi-spectral leaf imagers combined with machine learning as new tools for forest disease detection. Hyper- and multi-spectral images were collected from leaves of American elm genotypes with varied disease susceptibilities after mock-inoculation and inoculation with O. novo-ulmi under greenhouse conditions. Both traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image cubes. Deep learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting DED. Edges and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated trees. In addition, spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm genotypes. Though further studies are needed to assess applications in other pathosystems, hyper- and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees.

Diffractive multispectral optical imaging plays an essential role in optical sensing, which typically suffers from the image blurring problem caused by the spatially variant point spread function. Here, we propose a novel high-quality and efficient hybrid space calibrated 3D network “HSC3D” for spatially variant diffractive multispectral imaging that utilizes the 3D U-Net structure combined with space calibration modules of magnification and rotation effects to achieve high-accuracy eight-channel multispectral restoration. The algorithm combines the advantages of the space calibrated module and U-Net architecture with 3D convolutional layers to improve the image quality of diffractive multispectral imaging without the requirements of complex equipment modifications and large amounts of data. A diffractive multispectral imaging system is established by designing and manufacturing one diffractive lens and four refractive lenses, whose monochromatic aberration is carefully corrected to improve imaging quality. The mean peak signal-to-noise ratio and mean structural similarity index of the reconstructed multispectral images are improved by 3.33 dB and 0.08, respectively, presenting obviously improved image quality compared with a typical Unrolled Network algorithm. The new algorithm with high space calibrated ability and imaging quality has great application potential in diffraction lens spectroscopy and paves a new method for complex practical diffractive multispectral image sensing.

The tetrazolium (TZ) test is a reliable but destructive method for identifying viable seeds. In this study, a non-destructive seed viability analysis method for Allium ulleungense was developed using multispectral imaging and stacking ensemble learning. Using the Videometerlab 4, multispectral imaging data were collected from 390 A. ulleungense seeds subjected to NaCl-accelerated aging treatments with three repetitions per treatment. Spectral values were obtained at 19 wavelengths (365–970 nm), and seed viability was determined using the TZ test. Next, 80% of spectral values were used to train Decision Tree, Random Forest, LightGBM, and XGBoost machine learning models, and 20% were used for testing. The models classified viable and non-viable seeds with an accuracy of 95–91% on the K-Fold value (n = 5) and 85–81% on the test data. A stacking ensemble model was developed using a Decision Tree as the meta-model, achieving an AUC of 0.93 and a test accuracy of 90%. Feature importance and SHAP value assessments identified 570, 645, and 940 nm wavelengths as critical for seed viability classification. These results demonstrate that machine learning-based spectral data analysis can be effectively used for seed viability assessment, potentially replacing the TZ test with a non-destructive method.

Nematodes pose significant challenges for the fish processing industry, particularly in white fish. Despite technological advances, the industry still depends on manual labor for the detection and extraction of nematodes. This study addresses the initial steps of automatic nematode detection and differentiation from other common defects in fish fillets, such as skin remnants and blood spots. VideometerLab 4, an advanced Multispectral Imaging (MSI) System, was used to acquire 270 images of 50 Atlantic cod fillets under controlled conditions. In total, 173 nematodes were labeled using the Segment Anything Model (SAM), which is trained to automatically segment objects of interest from only few representative pixels. With the acquired dataset, we study the potential of identifying nematodes through their spectral signature. We incorporated normalized Canonical Discriminant Analysis (nCDA) to develop segmentation models trained to distinguish between different components within the fish fillets. By incorporating multiple segmentation models, we aimed to achieve a satisfactory balance between false negatives and false positives. This resulted in 88% precision and 79% recall for our annotated test data. This approach could improve process control by accurately identifying fillets with nematodes. Using MSI minimizes unnecessary inspection of fillets in good condition and concurrently boosts product safety and quality.

In this study, a novel method is proposed to estimate surface-spectral reflectance from camera responses that combine model-based and training-based approaches. An imaging system is modeled using the spectral sensitivity functions of an RGB camera, spectral power distributions of multiple light sources, unknown surface-spectral reflectance, additive noise, and a gain parameter. The estimation procedure comprises two main stages: (1) selecting the local optimal reflectance dataset from a reflectance database and (2) determining the best estimate by applying a neural network to the local optimal dataset only. In stage (1), the camera responses are predicted for the respective reflectances in the database, and the optimal candidates are selected in the order of lowest prediction error. In stage (2), most reflectance training data are obtained by a convex linear combination of local optimal data using weighting coefficients based on random numbers. A feed-forward neural network with one hidden layer is used to map the observation space onto the spectral reflectance space. In addition, the reflectance estimation is repeated by generating multiple sets of random numbers, and the median of a set of estimated reflectances is determined as the final estimate of the reflectance. Experimental results show that the estimation accuracies exceed those of other methods.

In recent decades scholars started reaching out to advanced imaging technologies to reveal hidden text of manuscripts or to identify features that are undetectable to the naked eye. To this end, Copenhagen, Den Arnamagnæanske Samling, AM 28 8vo, also known as Codex Runicus, one of the most famous and intriguing Danish medieval manuscripts written entirely in medieval runes, underwent multispectral imaging(MSI) and microscopic analysis. AM 28 8vo has been studied, edited and digitized, but no in-depth analysis of material features like erasures, changing of inks, missing or faded portions of texts by means of digital-based methods exists yet. Along with three scribes who wrote the texts, AM 28 8vo also includes a marginal apparatus that carries valuable information on the history of its ownership and its reading by leading Danish philologists. Some inks of both the main text and marginalia are examined here combining MSI and microscopic analyses to understand possible correlations between those who wrote, edited and annotated the texts directly on the manuscript. Finally, this contribution reports on the results of such analyses, taking spectral reflectance as a valid guide in a first attempt of mapping scribal hands, and demonstrates how it can expand our understanding of the manuscript, its production, and its history.

A novel multi-technique approach has been applied for the identification and mapping of wild orchids using a combination of remote sensing and spectral image analysis. The five orchid species identified were the common spotted-orchid (Dactylorhiza fuchsia), heath spotted-orchid (Dactylorhiza maculata), pyramidal orchid (Anacamptis pyramidalis), heath fragrant-orchid (Gymnadenia borealis), and the dark-red helleborine (Epipactis atrorubens). Field studies have been done using a hand-held spectrometer operating in the 400–700 nm visible spectrum, photogrammetry using a digital camera as well as a multispectral image camera operating at the specific spectral bands of 450 nm (blue), 560 nm (green), 650 nm (red), 730 nm (red edge) and 840 nm (near-infrared) attached to an unmanned aerial vehicle Data analysis, using the hand-held spectrometer, followed by pattern recognition using principal component analysis and partial least squares-discriminant analysis, have identified the key distinguishing wavelengths for identification of the 5 orchid types as 400, 410, 420 and 560 nm. The use of remote sensing, using the UAV-MSI, and application of a dedicated spectral index has enabled field identification of the orchids. Finally, object-based image analysis of field gathered photogrammetry imagery, has enabled use of shape, size, and color to identify and distinguish orchid species. The developed data analytic tool, using random forest classification, can be used to identify and characterize wild orchids across multiple sites within their short lifespan with an accuracy of 86%. Any longer-term study would provide invaluable information on the diversity and complexity of orchid habitat, population variation both intra- and inter-site location, as well as the impact of climate change.

Physical dormancy (PY) commonly present in the seeds of higher plants is believed to be responsible for the germination failure by impermeable seed coat in hard seeds of legume species, instead of physiological dormancy (PD). In this study, a non-destructive approach involving multispectral imaging was used to successfully identify hard seeds from non-hard seeds in Medicago sativa, with accuracy as high as 96.8%–99.0%. We further adopted multiple-omics strategies to investigate the differences of physiology, metabolomics, methylomics, and transcriptomics in alfalfa hard seeds, with non-hard seeds as control. The hard seeds showed dramatically increased antioxidants and 125 metabolites of significant differences in non-targeted metabolomics analysis, which are enriched in the biosynthesis pathways of flavonoids, lipids and hormones, especially with significantly higher ABA, a hormone known to induce dormancy. In our transcriptomics results, the enrichment pathway of “response to abscisic acid” of differential expressed genes (DEG) supported the key role of ABA in metabolomics results. The methylome analysis identified 54,899, 46,216 and 54,452 differential methylation regions for contexts of CpG, CHG and CHH, and 344 DEGs might be regulated by hypermethylation and hypomethylation of promoter and exon regions, including four ABA- and JA-responsive genes. Among 8% hard seeds in seed lots, 24.5% still did not germinate after scarifying seed coat, and were named as non-PY hard seeds. Compared to hard seeds, significantly higher contents of ABA/IAA and ABA/JA were identified in non-PY hard seeds, which indicated the potential presence of PD. In summary, the significantly changed metabolites, gene expressions, and methylations all suggested involvement of ABA responses in hard seeds, and germination failure of alfalfa hard seeds was caused by combinational dormancy (PY + PD), rather than PY alone.

Unexploded ordnance (UXO) poses a threat to soldiers operating in mission areas, but current UXO detection systems do not necessarily provide the required safety and efficiency to protect soldiers from this hazard. Recent technological advancements in artificial intelligence (AI) and small unmanned aerial systems (sUAS) present an opportunity to explore a novel concept for UXO detection. The new UXO detection system proposed in this study takes advantage of employing an AI-trained multi-spectral (MS) sensor on sUAS. This paper explores feasibility of AI-based UXO detection using sUAS equipped with a single (visible) spectrum (SS) or MS digital electro-optical (EO) sensor. Specifically, it describes the design of the Deep Learning Convolutional Neural Network for UXO detection, the development of an AI-based algorithm for reliable UXO detection, and also provides a comparison of performance of the proposed system based on SS and MS sensor imagery.

Spectral imaging allows to analyze optical tissue properties that are invisible to the naked eye. Different spectral setups have been introduced in the last years to allow intraoperative image-guidance. All systems suffer from different disadvantages, such as lack of real-time capability, limited spectral coverage or low resolution. We present a novel approach using two multispectral snapshot cameras covering different spectral ranges as a stereo-system. The proposed method allows for continuous hyperspectral imaging of the surgical scene, while also enabling 3D scene reconstruction. The study demonstrates the feasibility of this approach and its potential applications in real-time monitoring and tissue identification of surgical interventions. The paper also discusses the technical challenges and future directions for improving the imaging system.

Rice blast disease is one of the major diseases affecting rice plant, significantly impacting both yield and quality. Current detecting methods for rice blast disease mainly rely on manual surveys in the field and laboratory tests, which are inefficient, inaccurate, and limited in scale. Spectral and imaging technologies in the visible and near-infrared (Vis/NIR) region have been widely investigated for crop disease detection. This work explored the potential of integrating canopy reflectance spectra acquired near the ground and aerial multispectral images captured with an unmanned aerial vehicle (UAV) for estimating Soil-Plant Analysis Development (SPAD) values and detecting rice leaf blast disease in the field. Canopy reflectance spectra were preprocessed, followed by effective band selection. Different vegetation indices (VIs) were calculated from multispectral images and selected for model establishment according to their correlation with SPAD values and disease severity. The full-wavelength canopy spectra (450–850 nm) were first used for establishing SPAD inversion and blast disease classification models, demonstrating the effectiveness of Vis/NIR spectroscopy for SPAD inversion and blast disease detection. Then, selected effective bands from the canopy spectra, UAV VIs, and the fusion of the two data sources were used for establishing corresponding models. The results showed that all SPAD inversion models and disease classification models established with the integrated data performed better than corresponding models established with the single of either of the aerial and ground data sources. For SPAD inversion models, the best model based on a single data source achieved a validation determination coefficient (Rcv2) of 0.5719 and a validation root mean square error (RMSECV) of 2.8794, while after ground and aerial data fusion, these two values improved to 0.6476 and 2.6207, respectively. For blast disease classification models, the best model based on a single data source achieved an overall test accuracy of 89.01% and a Kappa coefficient of 0.86, and after data fusion, the two values improved to 96.37% and 0.95, respectively. These results indicated the significant potential of integrating canopy reflectance spectra and UAV multispectral images for detecting rice diseases in large fields.

Drought is a principal limiting factor in the production of agricultural crops; however, quinoa possesses certain adaptive and tolerance factors that make it a potentially valuable crop under drought-stress conditions. Within this context, the objective of the present study was to evaluate morphological and physiological changes in ten quinoa genotypes under three irrigation treatments: normal irrigation, drought-stress followed by recovery irrigation, and terminal drought stress. The experiments were conducted at the UNSA Experimental Farm in Majes, Arequipa, Peru. A series of morphological, physiological, and remote measurements were taken, including plant height, dry biomass, leaf area, stomatal density, relative water content, selection indices, chlorophyll content via SPAD, multispectral imaging, and reflectance measurements via spectroradiometry. The results indicated that there were numerous changes under the conditions of terminal drought stress; the yield variables of total dry biomass, leaf area, and plant height were reduced by 69.86%, 62.69%, and 27.16%, respectively; however, under drought stress with recovery irrigation, these changes were less pronounced with a reduction of 21.10%, 27.43%, and 17.87%, respectively, indicating that some genotypes are adapted or tolerant of both water-limiting conditions (Accession 50, Salcedo INIA and Accession 49). Remote sensing tools such as drones and spectroradiometry generated reliable, rapid, and precise data for monitoring stress and phenotyping quinoa and the optimum timing for collecting these data and predicting yield impacts was from 79–89 days after sowing (NDRE and CREDG r Pearson 0.85).

Breast cancer poses a global health challenge, yet the influence of ethnicity on the tumor microenvironment (TME) remains understudied. In this investigation, we examined immune cell infiltration in 230 breast cancer samples, emphasizing diverse ethnic populations. Leveraging tissue microarrays (TMAs) and core samples, we applied multiplex immunofluorescence (mIF) to dissect immune cell subtypes across TME regions. Our analysis revealed distinct immune cell distribution patterns, particularly enriched in aggressive molecular subtypes triple-negative and HER2-positive tumors. We observed significant correlations between immune cell abundance and key clinicopathological parameters, including tumor size, lymph node involvement, and patient overall survival. Notably, immune cell location within different TME regions showed varying correlations with clinicopathologic parameters. Additionally, ethnicities exhibited diverse distributions of cells, with certain ethnicities showing higher abundance compared to others. In TMA samples, patients of Chinese and Caribbean origin displayed significantly lower numbers of B cells, TAMs, and FOXP3-positive cells. These findings highlight the intricate interplay between immune cells and breast cancer progression, with implications for personalized treatment strategies. Moving forward, integrating advanced imaging techniques, and exploring immune cell heterogeneity in diverse ethnic cohorts can uncover novel immune signatures and guide tailored immunotherapeutic interventions, ultimately improving breast cancer management.

This paper presents the findings of the first multi-analytical investigation of the Book of Uí Mhaine, one of the largest Gaelic Books surviving from the medieval vernacular period. A combination of protein analysis, point X-ray fluorescence spectroscopy (XRF), multispectral imaging (MSI), point Fiber-Optic Reflectance Spectroscopy (FORS) and point Raman spectroscopy was used to perform a systematic investigation of the Book of Uí Mhaine’s constituent materials, including parchment, inks and pigments. The analysis revealed that the parchment was made of calfskin, both blunt tools and Pb-containing materials were used for ruling the pages throughout the manuscript, and iron-based inks were used to write the content of the book. The decoration was restricted to the initial letters and rubrication across the body text. The decoration color palette was limited to yellow and red, comprising arsenic-, mercury- and lead-based pigments. A copper-based green pigment was found only on one folio. The scientific knowledge acquired through this multi-analytical approach complemented a substantial corpus of knowledge already built by Gaelic scholars, paleographers, codicologists and conservators. This work not only allowed for the consolidation of existing information on methods and materials used for the production of medieval Gaelic manuscripts but also laid the basis for future comparative work with other contemporary traditions in Ireland and Europe.

Food fraud is widespread in the aquatic food market, hence fast and non-destructive methods of identification of fish flesh are needed. In this study, multispectral imaging (MSI) was used to screen flesh slices from 20 edible fish species commonly found in the sea around Yantai, China, by combining identification based on the mitochondrial COI gene. We found that nCDA images transformed from MSI data showed significant differences in flesh splices of the 20 fish species. We then employed eight models to compare their prediction performances based on the hold-out method with 70% training and 30% test sets. Convolutional neural network (CNN), quadratic discriminant analysis (QDA), support vector machine (SVM), and linear discriminant analysis (LDA) models perform well on cross-validation and test data. CNN and QDA achieved more than 99% accuracy on the test set. By extracting the CNN features for optimization, a very high degree of separation was obtained for all species. Furthermore, based on the Gini index in RF, 11 bands were selected as key classification features for CNN, and an accuracy of 98% was achieved. Our study developed a successful pipeline for employing machine learning models (especially CNN) on MSI identification of fish flesh, and provided a convenient and non-destructive method to determine the marketing of fish flesh in the future.

Background & Aims: Clinical trials for reducing fibrosis in steatotic liver disease (SLD) have targeted macrophages with variable results. We evaluated intrahepatic macrophages in patients with SLD to determine if activity scores or fibrosis stages influenced phenotypes and expression of druggable targets, such as CCR2 and galectin-3. Methods: Liver biopsies from controls or patients with minimal or advanced fibrosis were subject to gene expression analysis using nCounter to determine differences in macrophage-related genes (n = 30). To investigate variability among individual patients, we compared additional biopsies by staining them with multiplex antibody panels (CD68/CD14/CD16/CD163/Mac387 or CD163/CCR2/galectin-3/Mac387) followed by spectral imaging and spatial analysis. Algorithms that utilize deep learning/artificial intelligence were applied to create cell cluster plots, phenotype profile maps, and to determine levels of protein expression (n = 34). Results: Several genes known to be pro-fibrotic (e.g. CD206, TREM2, CD163, and ARG1) showed either no significant differences or significantly decreased with advanced fibrosis. Although marked variability in gene expression was observed in individual patients with cirrhosis, several druggable targets and their ligands (e.g. CCR2, CCR5, CCL2, CCL5, and LGALS3) were significantly increased when compared to patients with minimal fibrosis. Antibody panels identified populations that were significantly increased (e.g. Mac387+), decreased (e.g. CD14+), or enriched (e.g. interactions of Mac387) in patients that had progression of disease or advanced fibrosis. Despite heterogeneity in patients with SLD, several macrophage phenotypes and druggable targets showed a positive correlation with increasing NAFLD activity scores and fibrosis stages. Conclusions: Patients with SLD have markedly varied macrophage- and druggable target-related gene and protein expression in their livers. Several patients had relatively high expression, while others were like controls. Overall, patients with more advanced disease had significantly higher expression of CCR2 and galectin-3 at both the gene and protein levels. Impact and implications: Appreciating individual differences within the hepatic microenvironment of patients with SLD may be paramount to developing effective treatments. These results may explain why such a small percentage of patients have responded to macrophage-targeting therapies and provide additional support for precision medicine-guided treatment of chronic liver diseases.

Abstract Crop protection from diseases through applications of plant protection products is crucial to secure worldwide food production. Nevertheless, sustainable management of plant diseases is an open challenge with a major role in the economic and environmental impact of agricultural activities. A primary contribution is expected to come from precision crop protection approaches, with treatments tailored to spatial and time-specific needs of the crop, in contrast to the current practice of applying treatments uniformly to fields. In view of this, image-based automatic detection of early disease symptoms is considered a key enabling technology for high throughput scouting of the crop, in order to timely target the treatments on emerging infection spots. Thanks to the unprecedented performance in image-recognition problems, Deep Learning (DL) methods based on Convolutional Neural Networks (CNNs) have recently entered the domain of plant disease detection. This work develops two DL approaches for automatic recognition of powdery mildew disease on cucumber leaves, with a specific focus on exploring unsupervised techniques to overcome the need of large training set of manually labelled images. To this aim, autoencoder networks were implemented for unsupervised detection of disease symptoms through: i) clusterization of features in a compressed space; ii) anomaly detection. The two proposed approaches were applied to multispectral images acquired during in-vivo experiments, and the obtained results were assessed by quantitative indices. The clusterization approach showed only partially capability to provide accurate disease detection, even if it gathered some relevant information. Anomaly detection showed instead to possess a significant potential of discrimination which could be further exploited as a prior step to train more powerful supervised architectures with a very limited number of labelled samples.

Multispectral and polarized focusing and imaging are key functions that are vitally important for a broad range of optical applications. Conventional techniques generally require multiple shots to unveil desired optical information and are implemented via bulky multi-pass systems or mechanically moving parts that are difficult to integrate into compact and integrated optical systems. Here, a design of ultra-compact transversely dispersive metalens capable of both spectrum and polarization ellipticity recognition and reconstruction in just a single shot is demonstrated with both coherent and incoherent light. Our design is well suited for integrated and high-speed optical information analysis and can significantly reduce the size and weight of conventional devices while simplifying the process of collecting optical information, thereby promising for various applications, including machine vision, minimized spectrometers, material characterization, remote sensing, and other areas which require comprehensive optical analysis.

Digitalization techniques, such as photogrammetry (PG), are attracting the interest of experts in the cultural heritage field, as they enable the creation of three-dimensional virtual replicas of historical artifacts with 2D digital images. Indeed, PG allows for acquiring data regarding the overall appearance of an artifact, its geometry, and its texture. Furthermore, among several image-based techniques exploited for the conservation of works of art, multispectral imaging (MSI) finds great application in the study of the materials of historical items, taking advantage of the different responses of materials when exposed to specific wavelengths. Despite their great usefulness, PG and MSI are often used as separate tools. Integrating radiometric and geometrical data can notably expand the information carried by a 3D model. Therefore, this paper presents a novel research methodology that enables the acquisition of multispectral 3D models, combining the outcomes of PG and MSI (Visible (VIS), Ultraviolet-induced Visible Luminescence (UVL), Ultraviolet-Reflected (UVR), and Ultraviolet-Reflected False Color (UVR-FC) imaging) in a single coordinate system, using an affordable tunable set-up and open-source software. The approach has been employed for the study of two wooden artifacts from the Museo Egizio di Torino to investigate the materials present on the surface and provide information that could support the design of suitable conservation treatments.

The mosaic in the apse of the Church of S. Agnese fuori le mura in Rome represents one of the most important examples of the wall mosaics of the Roman Middle Ages. Although it is associated with Byzantine figurative culture, no scientific study has addressed this important piece of Italian art history. One factor that has probably limited and created difficulties for its analysis is the poor legibility of the original parts, which are compromised by heavy restorations affecting the gold background, the faces of the two male side figures, and the lower band of the mosaic. The present work describes how multispectral imaging provided significant guidance in the preliminary identification of possible original areas in this ancient wall mosaic. Through an interdisciplinary approach, the art historical background and historical graphic documentation of known restorations supported the use of multispectral imaging to recognize original parts. The initial results of the lab analyses (SEM-EDS and EMPA) of supposed original tesserae validated the hypothesis made a priori thanks to multispectral acquisition, opening up new application possibilities for use of this noninvasive technique in the preliminary in situ identification of original parts in restored glass wall mosaics.

Haploid breeding can shorten the breeding period of new maize varieties and is an important means to increase maize yield. In the breeding program, a large number of haploid seeds need to be screened, and this step is mainly achieved manually, which hinders the industrialization of haploid maize breeding. This article aims to develop a multispectral camera to identify the haploid seeds automatically. The camera was manufactured by replacing narrow-band filters of the ordinary CCD camera, and the RGB, 405 nm, 980 nm and 1050 nm images of haploid or diploid seeds were simultaneously captured (the characteristic wavelengths were determined according to color and high-oil markers of maize). The performance was tested using four maize varieties with the two genetic markers. The results show that the developed multispectral camera significantly improved the recognition accuracy of haploid maize seeds to 92.33%, 97.33%, 97% and 93.33% for the TYD1903, TYD1904, TYD1907 and TYD1908 varieties, respectively. The cameras in the near-infrared region (wavelengths of 980 nm and 1050 nm) achieved better performance for the varieties of high-oil marker, with an increase of 0.84% and 1.5%, respectively. These results demonstrate the strong potential of the multispectral imaging technology in the haploid seed identification of maize.

Palm oil has a bad reputation due to the exploitation of farmers and the destruction of endangered animal habitats. Therefore, many consumers wish to avoid the use of palm oil. Decorative sugar contains a small amount of palm oil to prevent the sugar from melting on hot bakery products. High-oleic sunflower oil used as a substitute for palm oil was analyzed in this study via multispectral imaging and an electronic nose, two methods suitable for potential large-batch analysis of sugar/oil coatings. Multispectral imaging is a nondestructive method for comparing the wavelength reflections of the surface of a sample. Reference samples enabled the estimation of the quality of unknown samples, which were confirmed via acid value measurements. Additionally, for quality determination, volatile compounds from decorative sugars were measured with an electronic nose. Both applications provide comparable data that provide information about the quality of decorative sugars.

Approaches for remote sensing can be used to estimate the influence of changes in environmental conditions on terrestrial plants, providing timely protection of their growth, development, and productivity. Different optical methods, including the informative multispectral and hyperspectral imaging of reflected light, can be used for plant remote sensing; however, multispectral and hyperspectral cameras are technically complex and have a high cost. RGB imaging based on the analysis of color images of plants is definitely simpler and more accessible, but using this tool for remote sensing plant characteristics under changeable environmental conditions requires the development of methods to increase its informativity. Our review focused on using RGB imaging for remote sensing the characteristics of terrestrial plants. In this review, we considered different color models, methods of exclusion of background in color images of plant canopies, and various color indices and their relations to characteristics of plants, using regression models, texture analysis, and machine learning for the estimation of these characteristics based on color images, and some approaches to provide transformation of simple color images to hyperspectral and multispectral images. As a whole, our review shows that RGB imaging can be an effective tool for estimating plant characteristics; however, further development of methods to analyze color images of plants is necessary.

Infrared multispectral imaging technology can achieve the long-distance, wide-ranging and fast detection of target gas, and has been widely used in the fields of dangerous-gas detection and environmental monitoring. However, due to the difficulty in acquiring background radiation as well as atmospheric disturbance and noise interference in the detection process, the quantitative detection of gas concentration has become a difficult problem to solve. Therefore, this paper proposes an inversion method for gas column concentration based on infrared multispectral imaging technology. Firstly, infrared background radiation images of the non-target gas absorption spectrum band were collected and converted into background radiation images of the target gas absorption spectrum band according to the dual-channel mapping relationship. Then, combined with the gas radiation images of the target gas absorption spectrum band, the column concentration distribution of the gas was obtained by using the measured calibration relationship between absorbance and column concentration. Experiments of gas detection in different environments were carried out, and the column concentration distribution of the target gas was inverted using this method; the results showed that the average relative error of the inversion of the gas column concentration was 4.84%, which enables the quantitative detection of gas column concentration in a complex environment.

Scanning micrο X-ray fluorescence (μ-XRF) and multispectral imaging (MSI) were applied to study philately stamps, selected for their small size and intricate structures. The μ-XRF measurements were accomplished using the M6 Jetstream Bruker scanner under optimized conditions for spatial resolution, while the MSI measurements were performed employing the XpeCAM-X02 camera. The datasets were acquired asynchronously. Elemental distribution maps can be extracted from the μ-XRF dataset, while chemical distribution maps can be obtained from the analysis of the multispectral dataset. The objective of the present work is the fusion of the datasets from the two spectral imaging modalities. An algorithmic co-registration of the two datasets is applied as a first step, aiming to align the multispectral and μ-XRF images and to adapt to the pixel sizes, as small as a few tens of micrometers. The dataset fusion is accomplished by applying k-means clustering of the multispectral dataset, attributing a representative spectrum to each pixel, and defining the multispectral clusters. Subsequently, the μ-XRF dataset within a specific multispectral cluster is analyzed by evaluating the mean XRF spectrum and performing k-means sub-clustering of the μ-XRF dataset, allowing the differentiation of areas with variable elemental composition within the multispectral cluster. The data fusion approach proves its validity and strength in the context of philately stamps. We demonstrate that the fusion of two spectral imaging modalities enhances their analytical capabilities significantly. The spectral analysis of pixels within clusters can provide more information than analyzing the same pixels as part of the entire dataset.

The application of remote sensing, which is non-destructive and cost-efficient, has been widely used in crop monitoring and management. This study used a built-in multispectral imager on a small unmanned aerial vehicle (UAV) to capture multispectral images in five different spectral bands (blue, green, red, red edge, and near-infrared), instead of satellite-captured data, to monitor soybean growth in a field. The field experiment was conducted in a soybean field at the Mississippi State University Experiment Station near Pontotoc, MS, USA. The experiment consisted of five cover crops (Cereal Rye, Vetch, Wheat, Mustard plus Cereal Rye, and native vegetation) planted in the winter and three fertilizer treatments (Fertilizer, Poultry Liter, and None) applied before planting the soybean. During the soybean growing season in 2022, eight UAV imaging flyovers were conducted, spread across the growth season. UAV image-derived vegetation indices (VIs) coupled with machine learning (ML) models were computed for characterizing soybean growth at different stages across the season. The aim of this study focuses on monitoring soybean growth to predict yield, using 14 VIs including CC (Canopy Cover), NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), EVI2 (Enhanced Vegetation Index 2), and others. Different machine learning algorithms including Linear Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) are used for this purpose. The stage of the initial pod development was shown as having the best predictability for earliest soybean yield prediction. CC, NDVI, and NAVI (Normalized area vegetation index) were shown as the best VIs for yield prediction. The RMSE was found to be about 134.5 to 511.11 kg ha−1 in the different yield models, whereas it was 605.26 to 685.96 kg ha−1 in the cross-validated models. Due to the limited number of training and testing samples in the K-fold cross-validation, the models’ results changed to some extent. Nevertheless, the results of this study will be useful for the application of UAV remote sensing to provide information for soybean production and management. This study demonstrates that VIs coupled with ML models can be used in multistage soybean yield prediction at a farm scale, even with a limited number of training samples.