Multispectral Imaging Technique Research Articles

Using the SCOPE model for potato growth, productivity and yield monitoring under different levels of nitrogen fertilization

Most applications of remote sensing in agricultural crop monitoring use multispectral imaging techniques, but with upcoming hyperspectral missions, the opportunity arises to better estimate pigment absorption and crop structure by exploiting the full solar reflective spectrum. In this study, we demonstrate how hyperspectral time series can be used with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model to estimate crop yield variability among fields, crop varieties and nitrogen treatments generically, i.e. without a calibration with in situ , data. Field experiments were conducted in two potato fields in the Netherlands between May and September 2019. The fields were planted with five varieties of potato, under three nitrogen fertilization treatments. By fitting the model to the full VNIR-SWIR spectrum of measured hyperspectral reflectance, we retrieved the model input parameters of Leaf Area Index (LAI), leaf chlorophyll content (Cab) and leaf water content (Cw) and simulated the photosynthesis throughout the season using data of local Automatic Weather Stations (AWS). Statistical analysis of measured and retrieved traits of LAI, Cab and canopy water content showed that two fields responded differently to the treatments, exhibiting fewer classes than were expected based on the experimental design. Potato yield, which was estimated as the sum of photosynthesis flux multiplied by the harvest index of 0.64, correlated with the measured tuber dry weight with R 2 0.36 and RMSE 2.5 t ha −1 . This study demonstrates that even in the absence of crop or variety specific information, hyperspectral reflectance and local weather data ingested into SCOPE can explain a substantial part of the observed variability in yield among fields. • Time series of hyperspectral canopy reflectance were recorded in two potato fields. • The variation in traits was created by nitrogen fertilization treatments. • SCOPE model retrieved plant traits contained information about treatments. • SCOPE-simulated accumulated photosynthesis correlated with the final dry yield. • SCOPE model is more generic than dimensionality reduction methods.

Experimental detection of two-dimensional temperature distribution in Rocket-Based Combined Cycle combustion chamber using multispectral imaging processing

The extremely harsh environment and highly unsteady and transient nature during supersonic combustion bring great challenges for quantitative diagnostics. In the present work, for the first time, we report measurements of two-dimensional distributions of flame temperatures in a Rocket-Based Combined Cycle (RBCC) combustion chamber using a multispectral imaging technique with both spatial and spectral resolutions. The relation between the multispectral radiation intensity images and the flame 2-D temperature distributions was established by combing the Lvevenberg-Marquardt algorithm and the Hottel and Broughton emissivity models. The experimental results revealed that both laser-induced plasma (LIP) and plasma jet (PJ) torches had positive effects on combustion stabilization, and LIP had better effect on combustion enhancement. In the ignition stage of a fuel/air mixture with an equivalence ratio of 1.0, the average temperature of a flame initiated by LIP combustion enhancement was 100 K higher than that by PJ torch combustion enhancement. A recirculation zone was formed inside a cavity, which improved the combustion stability. The injected fuel and inflow were most fully mixed on the slope at the rear of the cavity, where 70 % and 65 % of the peak flame temperature points of LIP and JP were located, respectively. The combustion in the RBCC chamber showed an oscillation phenomenon that peak flame temperature and the flame structure oscillated continuously around the central axis with a horizontal extent of 162 mm. Due to the insufficient fuel blending, the oscillation amplitude was minimal when the equivalence ratio was 1.0. Overall, the results of flame temperature distributions demonstrated that the proposed multispectral thermometry was suitable for measuring temperatures of supersonic combustion flames.

Validating the accuracy of multispectral metal artifact suppressed diffusion-weighted imaging.

Diffusion-weighted imaging (DWI) provides quantitative measurement of random water displacement in tissue as calculated by the apparent diffusion coefficient (ADC). While heavily utilized in stroke and oncology applications, DWI is a promising tool to map microstructural changes in musculoskeletal applications including evaluation of synovial reactions resulting from total hip arthroplasty (THA). One major challenge facing the application of DWI in THA is the significant artifacts related to the conventional echo-planar imaging (EPI) readout used. Multispectral imaging (MSI) techniques, including the multiacquisition with variable resonance image combination (MAVRIC), have been shown to effectively reduce metallic susceptibility artifacts around total joint replacements to render clinically useful images. Recently, a 2D periodically rotated overlapping parallel line with enhanced reconstruction (PROPELLER) FSE acquisition that incorporates a diffusion preparation pulse with 2D-MAVRIC has been developed to mitigate both distortion and dropout artifacts. While there have been some preliminary assessments of DWI-MAVRIC, the repeatability of DWI-MAVRIC and the effects of key parameters, such as the number of spectral bins, are unknown. To evaluate the quantitative accuracy of DWI-MAVRIC as compared to conventional diffusion sequences. A diffusion phantom with different reference diffusivities (ADC = 113-1123 μm2 /s) was used. Scans were performed on two 1.5T MRI scanners. DWI-EPI and DWI-MAVRIC were acquired in both the axial and coronal planes. Three spatial offsets (0 cm, 10 cm left, and 10 cm right off iso-center) were used to evaluate effects of off-isocenter positioning. To assess intraday and interday repeatability, DWI-EPI and DWI-MAVRIC acquisitions were repeated on one scanner at same-day and 9-month intervals. To assess inter-scanner repeatability, DWI-EPI and DWI-MAVRIC acquisitions were compared between two scanners. ADC maps were generated with and without gradient nonlinearity correction (GNC). Linear regression, correlation, and error statistics were determined between calculated and reference ADC values. Bland-Altman plots were generated to evaluate intraday, interday, and interscanner repeatability. DWI-MAVRIC had excellent correlation to reference values but at reduced linearity (r = 1.00, slope = 0.91-0.94) as compared to DWI-EPI (r = 1.00, slope = 0.99-1.01). A greater than 5% ADC bias was observed at the lowest ADC values, predominantly in the DWI-MAVRIC scans. ADC values did not vary with DWI-MAVRIC parameters. DWI-EPI acquisitions had intraday, interday, and interscanner repeatability of 3.18 μm2 /s, 19.2 μm2 /s, and 20.2 μm2 /s, respectively. DWI-MAVRIC acquisitions had inferior intraday, interday, and interscanner repeatability of 13.3 μm2 /s, 44.7 μm2 /s, 110 μm2 /s, respectively. Lower ADC errors were found at isocenter, as compared to the left and right positions. GNC reduced the absolute error by 0.31% ± 0.89%, 3.6% ± 1.4%, 0.65% ± 2.4% for the center, left, and right positions, respectively. DWI-MAVRIC provides good linearity with respect to reference values and good intra- and interday repeatability.

Application of Fourier Transform Infrared (FT-IR) Spectroscopy, Multispectral Imaging (MSI) and Electronic Nose (E-Nose) for the Rapid Evaluation of the Microbiological Quality of Gilthead Sea Bream Fillets.

The potential of Fourier transform infrared (FT-IR) spectroscopy, multispectral imaging (MSI), and electronic nose (E-nose) was explored in order to determine the microbiological quality of gilthead sea bream (Sparus aurata) fillets. Fish fillets were maintained at four temperatures (0, 4, 8, and 12 °C) under aerobic conditions and modified atmosphere packaging (MAP) (33% CO2, 19% O2, 48% N2) for up to 330 and 773 h, respectively, for the determination of the population of total viable counts (TVC). In parallel, spectral data were acquired by means of FT-IR and MSI techniques, whereas the volatile profile of the samples was monitored using an E-nose. Thereafter, the collected data were correlated to microbiological counts to estimate the TVC during fish fillet storage. The obtained results demonstrated that the partial least squares regression (PLS-R) models developed on FT-IR data provided satisfactory performance in the estimation of TVC for both aerobic and MAP conditions, with coefficients of determination (R2) for calibration of 0.98 and 0.94, and root mean squared error of calibration (RMSEC) values of 0.43 and 0.87 log CFU/g, respectively. However, the performance of the PLS-R models developed on MSI data was less accurate with R2 values of 0.79 and 0.77, and RMSEC values of 0.78 and 0.72 for aerobic and MAP storage, respectively. Finally, the least satisfactory performance was observed for the E-nose with the lowest R2 (0.34 and 0.17) and the highest RMSEC (1.77 and 1.43 log CFU/g) values for aerobic and MAP conditions, respectively. The results of this work confirm the effectiveness of FT-IR spectroscopy for the rapid evaluation of the microbiological quality of gilthead sea bream fillets.

Development of Simplified Models for Non-Destructive Hyperspectral Imaging Monitoring of S-ovalbumin Content in Eggs during Storage.

S-ovalbumin content is an indicator of egg freshness and has an important impact on the quality of processed foods. The objective of this study is to develop simplified models for monitoring the S-ovalbumin content of eggs during storage using hyperspectral imaging (HSI) and multivariate analysis. The hyperspectral images of egg samples at different storage periods were collected in the wavelength range of 401–1002 nm, and the reference S-ovalbumin content was determined by spectrophotometry. The standard normal variate (SNV) was employed to preprocess the raw spectral data. To simplify the calibration models, competitive adaptive reweighted sampling (CARS) was applied to select feature wavelengths from the whole spectral range. Based on the full and feature wavelengths, partial least squares regression (PLSR) and least squares support vector machine (LSSVM) models were developed, in which the simplified LSSVM model yielded the best performance with a coefficient of determination for prediction (R2P) of 0.918 and a root mean square error for prediction (RMSEP) of 7.215%. By transferring the quantitative model to the pixels of hyperspectral images, the visualizing distribution maps were generated, providing an intuitive and comprehensive evaluation for the S-ovalbumin content of eggs, which helps to understand the conversion of ovalbumin into S-ovalbumin during storage. The results provided the possibility of implementing a multispectral imaging technique for online monitoring the S-ovalbumin content of eggs.

Cephalopod-inspired snapshot multispectral sensor based on geometric phase lens and stacked organic photodetectors

Multispectral imaging (MSI) is a valuable sensing modality for applications that require detecting a scene’s chemical characteristics. Existing MSI techniques utilize a filter wheel or color filter arrays, which are subject to reduced temporal or spatial resolution. In this work, we present a cephalopod-inspired multispectral organic sensor (CiMOS) based on geometric phase lenses (GPLs) and organic photovoltaics (OPVs) to enable aberration-based color sensing. We mimic the approach by which animals with single-type photoreceptors perceive colors via chromatic aberration. The intrinsic chromatic aberration of GPLs allows for multispectral sensing by stacking precisely patterned OPVs within specific spectrally dependent focal lengths. We provide simulations and a proof of concept of the CiMOS and highlight its advantages, including its simple design and snapshot multi-color detection using only a single axial position. Experimental results demonstrate the sensor’s ability to detect four colors with full width at half maximum spectral resolution as low as 35 nm.

Underwater Multispectral Laser Serial Imager for Spectral Differentiation of Macroalgal and Coral Substrates

The advancement of innovative underwater remote sensing detection and imaging methods, such as continuous wave laser line scan or pulsed laser (i.e., LiDAR—Light Detection and Ranging) imaging approaches can provide novel solutions for studying biological substrates and manmade objects/surfaces often encountered in underwater coastal environments. Such instruments can be used shipboard or coupled with proven and available deployment platforms as AUVs (Autonomous Underwater Vehicles). With the right planning, large areas can be surveyed, and more extreme and difficult-to-reach environments can be studied. A prime example, and representing a certain navigational challenge, is the under ice in the Arctic/Antarctic or winter/polar environments or deep underwater survey. Among many marine biological substrates, numerous species of macroalgae can be found worldwide in shallow down to 70+ m (clear water) coastal habitats and are essential ecosystem service providers through the habitat they provide for other species, the potential food resource value, and carbon sink they represent. Similarly, corals also provide important ecosystem services through their structure and diversity, are found to harbor increased local diversity, and are equally valid targets as “keystone” species. Hence, we expand current underwater remote sensing methods to combine macroalgal and coral surveys via the development of a multispectral laser serial imager designed for classification via spectral response. By using multiple continuous wave laser wavelength sources to scan and illuminate recreated benthic environments composed of macroalgae and coral, we show how elastic (i.e., reflectance) and inelastic (i.e., fluorescence) spectral responses can potentially be used to differentiate algal color groups and certain coral genus. Experimentally, three laser diodes (450 nm, 490 nm, 520 nm) are sequentially used in conjunction with up to 5 emission filters (450 nm, 490 nm, 520 nm, 580 nm, 685 nm) to acquire images generated by laser line scan pattern via high-speed galvanometric mirrors. Placed directly adjacent to a large saltwater imaging tank fitted with optical viewports, the optical system records target substrate spectral response using a photomultiplier preceded by a filter and is synchronously digitized to the scan rate by a high sample rate Analog-to-Digital Converter (ADC). Acquired images are normalized to correct for imager optical effects allowing for fluorescence intensity-based pixel segmentation via intensity thresholding. Overall, the multispectral laser serial imaging technique shows that the resulting high resolution data can be used for detection and classification of benthic substrates by their spectral response. These methods highlight a path towards eventual pixel-wise spectral response analysis for spectral differentiation.

Application of multispectral imaging combined with machine learning models to discriminate special and traditional green coffee

Non-destructive techniques aided by machine learning models are widely implemented in food analysis. To discriminate between ‘special’ and ‘traditional’ classes of green coffee beans, an advanced multispectral imaging technique based on reflectance and autofluorescence data was employed in combination with four machine learning algorithms (SVM, RF, XGBoost, and CatBoost). Of the four algorithms, SVM showed superior accuracy (0.96) for the test dataset. Analysis using PCA and SVM algorithms showed that autofluorescence data from excitation/emission combination of 405/500 nm contributed most to the discrimination of special green coffee from the traditional class. Fluorophores that can be linked to green fluorescence, namely catechin, caffeine and 4-hydroxybenzoic, synapic and chlorogenic acids, were found to have a considerable influence on the differentiation of specialty and traditional coffees. Analysis based on multispectral autofluorescence imaging combined with SVM models was proven to be a valuable tool for future applications in the food industry for the non-destructive and real-time classification of special and traditional green coffee.

Wavelength and frequency optimization in spatial frequency domain imaging for two-layer tissue

Spatial frequency domain imaging is a non-contact, wide-field, fast-diffusion optical imaging technique, which in principle uses steady-state spatially modulated light to irradiate biological tissue, reconstruct two-dimensional or three-dimensional tissue optical characteristic map through optical transmission model, and further quantify the spatial distribution of tissue physiological parameters by multispectral imaging technique. The selection of light source wavelength and light field spatial modulation frequency is directly related to the accuracy of tissue optical properties and tissue physiological parameters extraction. For improvement of the measurement accuracy of optical properties and physiological parameters in the two-layer tissue, a multispectral spatial frequency domain imaging system is built based on liquid crystal tunable filter, and a data mapping table of spatially resolved diffuse reflectance and optical properties of two-layer tissue is established based on scaling Monte Carlo method. Combined with the dispersion effect and window effect of light-tissue interaction, the study applies numerical simulation to optimize the wavelength in the 650-850 nm range with spectral resolution of 10 nm. In order to minimize the uncertainty of the optical properties, Cramér-Rao bound is used to optimize the optical field spatial modulation frequency by transmitting the uncertainty of optical properties. The results showed that in order to realize the detection of melanin, oxyhemoglobin, deoxyhemoglobin, water and other physiological parameters in two-layer tissue, the best wavelength combination was determined as 720, 730, 760 and 810 nm according to the condition number. The findings of the Cramér-Rao bound analysis reveal that the uncertainty of optical characteristics for the frequency combinations [0, 0.3] mm-1, [0, 0.2] mm-1, and [0, 0.1] mm-1 increases successively. Under the optimal combination of wavelength and frequency, the diffuse reflectance of the gradient gray-scale plate measured by the multi-spectral spatial frequency domain imaging system is linearly correlated with the calibration value. The error between the measured liquid phantom absorption coefficient and the collimation projection system based on colorimetric dish is less than 2%. The experimental results of human brachial artery occlusion indicate that under the optimal wavelength combination, the change of the second layer absorption coefficient captured by the three frequency combinations decreases in turn, so as the change of oxygen saturation.

On the measurement of flame temperature and emissivity based on multispectral imaging technique

Due to the low spatial resolution of spectrometer and low spectral resolution of CCD, a multi-wavelength radiation thermometry method employing multispectral imaging technology was proposed to measure flame temperature and emissivity. The calculated radiation intensity lines were obtained by the Hottel emissivity model and the least square method. The temperature and flame absorption capacity with the calculated spectral line closest to the measured spectral line were considered as the optimum results. The experimental results showed that maximum temperature of the flame was 1943 K appeared in the thin-wick paraffin. At any position, the emissivity of the thin-wick paraffin flame was lower than that of the thick-wick, but the emissivity of the biomass candle flame near the flame center was the highest. The measured temperature distribution was validated by employing a spectrometer system and the relative error was 2.66%.

Pterygium and Ocular Surface Squamous Neoplasia: Optical Biopsy Using a Novel Autofluorescence Multispectral Imaging Technique.

Simple SummaryCancer is able to damage the surface of the eye, especially in countries like Australia with high exposure to ultraviolet radiation from the sun. Such cancer (ocular surface squamous neoplasia or OSSN) is similar in appearance to a common and benign eye disease called pterygium. Currently, eye biopsy is the gold standard diagnostic method for OSSN, which is traumatic for the patient, carries risks, and has been potentially unnecessary in patients diagnosed with pterygium only. This research introduces an imaging-based method for OSSN screening, which will reduce pressure on health resources—and hopefully eliminate the need for eye biopsy in cases of suspected OSSN.In this study, differentiation of pterygium vs. ocular surface squamous neoplasia based on multispectral autofluorescence imaging technique was investigated. Fifty (N = 50) patients with histopathological diagnosis of pterygium (PTG) and/or ocular surface squamous neoplasia (OSSN) were recruited. Fixed unstained biopsy specimens were imaged by multispectral microscopy. Tissue autofluorescence images were obtained with a custom-built fluorescent microscope with 59 spectral channels, each with specific excitation and emission wavelength ranges, suitable for the most abundant tissue fluorophores such as elastin, flavins, porphyrin, and lipofuscin. Images were analyzed using a new classification framework called fused-classification, designed to minimize interpatient variability, as an established support vector machine learning method. Normal, PTG, and OSSN regions were automatically detected and delineated, with accuracy evaluated against expert assessment by a specialist in OSSN pathology. Signals from spectral channels yielding signals from elastin, flavins, porphyrin, and lipofuscin were significantly different between regions classified as normal, PTG, and OSSN (p < 0.01). Differential diagnosis of PTG/OSSN and normal tissue had accuracy, sensitivity, and specificity of 88 ± 6%, 84 ± 10% and 91 ± 6%, respectively. Our automated diagnostic method generated maps of the reasonably well circumscribed normal/PTG and OSSN interface. PTG and OSSN margins identified by our automated analysis were in close agreement with the margins found in the H&E sections. Such a map can be rapidly generated on a real time basis and potentially used for intraoperative assessment.

Multispectral Wavebands Selection for the Detection of Potential Foreign Materials in Fresh-Cut Vegetables.

Ensuring the quality of fresh-cut vegetables is the greatest challenge for the food industry and is equally as important to consumers (and their health). Several investigations have proven the necessity of advanced technology for detecting foreign materials (FMs) in fresh-cut vegetables. In this study, the possibility of using near infrared spectral analysis as a potential technique was investigated to identify various types of FMs in seven common fresh-cut vegetables by selecting important wavebands. Various waveband selection methods, such as the weighted regression coefficient (WRC), variable importance in projection (VIP), sequential feature selection (SFS), successive projection algorithm (SPA), and interval PLS (iPLS), were used to investigate the optimal multispectral wavebands to classify the FMs and vegetables. The application of selected wavebands was further tested using NIR imaging, and the results showed good potentiality by identifying 99 out of 107 FMs. The results indicate the high applicability of the multispectral NIR imaging technique to detect FMs in fresh-cut vegetables for industrial application.

Review on Optical Imaging Techniques for Multispectral Analysis of Nanomaterials.

Biomedical imaging is an essential tool for investigating biological responses in vivo. Among the several imaging techniques, optical imaging systems with multispectral analysis of nanoparticles have been widely investigated due to their ability to distinguish the substances in biological tissues in vivo. This review article focus on multispectral optical imaging techniques that can provide molecular functional information. We summarize the basic principle of the spectral unmixing technique that enables the delineation of optical chromophores. Then, we explore the principle, typical system configuration, and biomedical applications of the representative optical imaging techniques, which are fluorescence imaging, two-photon microscopy, and photoacoustic imaging. The results in the recent studies show the great potential of the multispectral analysis techniques for monitoring responses of biological systems in vivo.

Ghosts, Holes, Rips and Scrapes: Shakespeare in 1619; Bibliography in the Longue Durée. Zachary Lesser. Philadelphia: University of Pennsylvania Press, 2021. Pp. x+198.

<i>Ghosts, Holes, Rips and Scrapes: Shakespeare in 1619; Bibliography in the Longue Durée</i>. Zachary Lesser. Philadelphia: University of Pennsylvania Press, 2021. Pp. x+198.

Predicting soluble solids content in “Fuji” apples of different ripening stages based on multiple information fusion

Soluble solids content (SSC) is one of the important components for the assessment of fruit quality. Near-infrared (NIR) spectroscopy, multispectral and hyperspectral imaging techniques are widely used methods for the SSC estimation. However, the high prices are unaffordable for small commercial orchards. The objective of this work is to present a computer vision system for the SSC estimation of “Fuji” apples in different ripening stages. Multiple information of the color channels (channel a* and channel b*) in L*a*b* color space translated from RGB images were applied to extract color features. Stacked autoencoder (SAE) algorithm was used to extract color features in pixel-level, and the trained parameters were used as the initial parameters of the prediction model by using back propagation neural network (BPNN) algorithm. The results show that the prediction accuracy of the SAE-BPNN model based on color features in pixel-level is higher than that of the BPNN model based on pure color features in feature-level. Moreover, the model with fusion information of both color channels as inputs can get better prediction results than that of single channel. The SAE-BPNN model with SAE structure of 270-120-30 yielded the best result with Rp2 = 0.5953 and RMSEP = 0.8856%. This research indicates that RGB imaging technology based on multiple information has potential for the SSC prediction, which provides reference values for fruit harvesting and non-destructive evaluation of fruit quality.

Multispectral Imaging and p-XRF for the Non-Invasive Characterization of the Anonymous Devotional Painting ‘Maria Santissima delle Grazie’ from Mirabella Imbáccari (Sicily, Italy)

This work presents the results of the in situ, non-invasive diagnostic investigations performed on the canvas oil painting depicting Madonna and Child, venerated as ‘Maria Santissima delle Grazie’ by the local religious community. The work of art (72 cm × 175 cm) is located on the high altar of the main Church in Mirabella Imbáccari, near Catania (Sicily, Italy). The painter is anonymous, and the supposed dating is the late eighteenth century. Although the painting has never been studied before, it has been attributed to a Sicilian workshop in the literature, raising the doubts of the art historian who conducted this study and who hypothesized a Neapolitan manufacture. Furthermore, due to the good conservation state detected by a macroscopic examination, doubts also arose about dating. To shed light on these aspects, a technical-scientific examination proved necessary. Multispectral imaging techniques (IR Reflectography, UV-induced visible Fluorescence, X-ray) are carried out for the study of the execution technique, the identification of underlying remakes, sketch drawing and the evaluation of the conservation conditions. XRF spectrometry analysis is performed for the identification of the chemical elements constituting the pigments (inorganic chromophores). The diagnostic results allowed this research to confirm the dating suggested by the historical-stylistic knowledge and to highlight new technical peculiarities supporting the attribution to a Neapolitan workshop.

Evaluating Japanese dace (Tribolodon hakonensis) fish freshness during storage using multispectral images from visible and UV excited fluorescence

This research aimed at providing a quick, and non-destructive method for estimating freshness of intact Japanese dace (Tribolodon hakonensis) fish refrigerated below 5 °C using multispectral imaging technique coupled with multivariate analysis techniques. The fluorescence excitation-emission matrices (EEMs) for outside parts of the fish (eyeball, scales) were taken to establish the proper excitation wavelengths. Afterwards, four lighting devices; white, 395 nm, 365 nm and 280 nm LEDs were used to illuminate the sample for capturing images using both the UV camera and a common color camera. Biochemical analysis (electrophoresis measurement) was carried out to examine fish freshness and then expressed using a reference method as K values. Both EEM and imaging data were modelled with the measured K value using partial least square (PLS) regression. A novel algorithm based on multispectral imaging data was proposed as a potential fish freshness indicator during storage. From the results, R2 of 0.94 and RMSE of 2.42% were achieved. This research demonstrated that the fluorescence multispectral imaging technique is a powerful tool with great potential in non-destructive monitoring and examining fish freshness during storage.

Analysis of Compound Stained Cervical Cell Images Using Multi-Spectral Imaging

Multi-spectral imaging technique plays an important role in real-world applications such as medicine and medical detections. This paper proposes a cervical cancer cell screening method to simultaneously adopt TBS classification and DNA quantitative analysis for a single cell smear. Through using compound staining on a smear, the cytoplasm is stained by Papanicolauo and the nucleus is stained by Feulgen. The main evaluation parameter is the DNA content of the nucleus, not the subjective description of cell morphology, which is more objective than the TBS classification method and reduces the chances of missing a diagnosis due to subjective factors. Each nucleus has its own DI value and color image of the whole cell, which is convenient for doctors as it allows them to review and confirm the morphology of cells with a nucleus DI of over 2.5. Mouse liver smears and cervical cases are utilized as the measuring specimens to evaluate the performance of the microscope multi-spectral imaging system; illustrative results demonstrate that the proposed system qualifies, with high accuracy and reliability, and further presents wide application prospects in the early diagnosis of cervical cancer.

Apple powdery mildew infestation detection and mapping using high-resolution visible and multispectral aerial imaging technique

Powdery mildew (PM) in apple orchards is a critical fungal disease that considerably reduces yield, harvested fruit quality, and orchard health. Rapid detection and mapping of resulting infestation at orchard scale is a challenge with existing laborious manual scouting approaches. Therefore, this study explored the feasibility of detecting and mapping PM infestation in an apple orchard block using high-resolution visible (red-green-blue [RGB]) and multispectral imaging technique. Imaging campaigns were conducted over an experimental orchard using small unmanned aerial systems (UAS) integrated with the above optical sensors. K-means classifier trained on individual snapshots of RGB imagery had mean disease detection accuracy of 77%. Eight multispectral vegetation indices also showed significant differences (p < 0.001) between healthy (Mean: 0.25–0.84) and infected (Mean: 0.01–0.25) leaves. Modified Simple Ratio-Red (MSRR), Modified Simple Ratio-Blue (MSRB), and Optimized Soil Adjusted Vegetation Index (OSAVI) showed the highest contrast (Mean: 0.46–0.79). Orchard block-scale mapping using RGB orthomosaic layer, classified with k-means and spectral angle mapper techniques had accuracies up to 73%. Overall, high resolution imagery in the visible and multispectral domain could sufficiently detect the infection and generate site-specific disease heat maps. These maps could be useful to apple growers in directing prescriptive resources ( pruning labor or fungicide application) for PM management.

Revealing time\u2019s secrets at the National Theatre of Costa Rica via innovative software for cultural heritage research

Establishing affordable, efficient, accessible, innovative, and multidisciplinary methodologies to the diagnosis of the conservation state of an artwork is key to carry out appropriate strategies of conservation and consequently to the creation of modern public policies on cultural heritage. Limited access to large-format paintings is a challenge to restoration scientists seeking to obtain information quickly, in a non-destructive and non-invasive manner, and identify regions of interest. Therefore, we put forward two unique software tools based on multispectral imaging techniques, with the long-term aim to assess the artist’s intentions, creative process, and colour palette. This development paves the way for a comprehensive and multidisciplinary understanding of the mysteries encompassed in each pictorial layer, through the study of their physical and chemical characteristics. We conducted the first ever study on Musas I and Musas II, two large-format paintings by Italian artist Carlo Ferrario, located in the National Theatre of Costa Rica. In this study, we used our novel imaging techniques to choose regions of interest in order to study sample layers; while also assessing the works’ state of conservation and possible biodeterioration. We explored the applications of our two versatile software tools, RegionOfInterest and CrystalDistribution, and confirmed paint stratigraphies by means of microscopy and spectroscopy analyses (OM, SEM-EDX, Fluorescent microscopy, FTIR-ATR and micro-Raman). In a pilot study, we identified the artist’s main colour palette: zinc white, lead white, chrome yellow, lead read, viridian, along with artificial vermilion and ultramarine pigments. We were able to identify artificial vermilion and ultramarine and distinguish them from the natural pigments using CrystalDistribution to map the average size and diameter of the pigment crystals within the paint layers. This study demonstrated that software-based multidisciplinary imaging techniques are novel in establishing preventive and non-invasive methods for historical painting conservation studies, in addition, this study provides tools with great potential to be used in the future in applications such as virtual restoration.