Multispectral Image Analysis Research Articles

Multispectral image analysis detects differences in drought responses in novel seeded Miscanthus sinensis hybrids

AbstractDrought tolerance in Miscanthus sinensis is a desirable trait because of its potential use to develop new varieties, namely intra‐specific hybrids, adapted to drought‐prone marginal lands. In this study, drought tolerance was evaluated on 8 M. sinensis intra‐specific hybrids (GRC1–GRC8). Plants were grown in the growth chamber (14/10 h, 25/20°C, 70% relative air humidity and 300 μmol m−2 s−1 irradiance). Drought was induced by withholding irrigation for 21 days (stress phase) and after re‐watering (recovery phase) for 7 days. Nondestructive multispectral 3D images for plant morphology, color, and chlorophyll fluorescence imaging were used to quantify drought‐induced changes on a weekly basis for the entire duration of the experiment. Total leaf area (TLA) and digital biomass (DB) responded most rapidly to water deficits (7–14 days), followed by leaf senescence (14–21 days), and finally, a drop in the maximum efficiency of PSII (Fv/Fm; 21 days). Traits measured on the last day of the drought treatment were used to calculate the phenotypic plasticity. Significant differences in drought susceptibility and phenotypic plasticity were found among the studied hybrids. Drought treatment (21 days) reduced DB and TLA on average by 50%, normalized difference vegetation index (NDVI) by 20% and Fv/Fm from 0.79 (in control) to 0.69 in the less drought‐susceptible hybrids (GRC4 and GRC5), whereas in drought‐sensitive hybrids (GRC2 and GRC3), 21 days of drought reduced DB and TLA on average by 80%, NDVI by 45% and Fv/Fm dropped from 0.79 (in control) to 0.35. The more drought‐resilient hybrids showed lower phenotypic plasticity than their more sensitive counterparts, a stay‐green strategy enabled through lower biomass accumulation and by extension, reduced water usage. Multispectral imaging and image analysis enabled fast and nondestructive quantification of plant morphological and physiological responses under drought conditions and could be used as an effective screening tool for drought susceptibility.

Natural hydrogen emanations in Namibia: Field acquisition and vegetation indexes from multispectral satellite image analysis

Natural hydrogen exploration is now active in various places of the world. Onshore, correlation between natural H2 generation and the presence of iron rich rocks especially from Archean and Neoproterozoic cratons have been observed. Emanations and accumulations of H2 have already been confirmed in such geological settings in Australia, South Africa and Brazil. The geological similitude and the presence of numerous sub circular depressions that are a good proxy for hydrogen emanations suggest that hydrogen resources may also exist in Namibia. We present here the results of a data acquisition campaign which allowed us to confirm the presence of natural hydrogen in this country in the vicinity of Neoproterozoic Banded Iron Formation. The H2 content in the soil, as in Brazil, is variable within the depressions in time and space and is particularly time sensitive across the day. Comparison of the H2 signal versus time within these two regions shows a similar behavior of the soils with an increase of the H2 flow at the middle of the day. In addition, these new data allow us to better constrain the morphological characteristics of such H2-emiting depressions. By using satellite images and digital elevation model we propose a new proxy to differentiate potentially H2-emiting features from other type of depressions such as Salt Pan. The Landsat multispectral images and their processing through NDVI and SAVI indexes, that highlight a ring of healthy vegetation around the sub circular area with scarce vegetation already observed appear able to discriminate between H2 emitting structures and other soft depressions.

Spectral classification of space agricultural landscape images using ranking of channels according to the highest informativity

This paper explores the question of classification of a series of space images for analysis of the processes of growing crops, using the spectral channels of minimum dimension. This study attempts to give a partial answer to the question: how to detect the problems in growing processes much faster and with a high level of accuracy? In this paper, we report on the results of the survey conducted to determine the existing methods and tools for spectral analysis of multispectral space images for solving forecasting and search problems of mapping the agricultural objects. The present work extends the use of the general image classification process by the computational procedure of ranking spectral channels according to the value of spectral information. The structure of the classification rule based on the informativity indicator of the spectral channel has been developed. The proposed prototype of a geospatial recognition system will provide a digital stratification landscape plan.

Predicting Sugarcane Biometric Parameters by UAV Multispectral Images and Machine Learning

Multispectral sensors onboard unmanned aerial vehicles (UAV) have proven accurate and fast to predict sugarcane yield. However, challenges to a reliable approach still exist. In this study, we propose to predict sugarcane biometric parameters by using machine learning (ML) algorithms and multitemporal data through the analysis of multispectral images from UAV onboard sensors. The research was conducted on five varieties of sugarcane, as a way to make a robust approach. Multispectral images were collected every 40 days and the evaluated biometric parameters were: number of tillers (NT), plant height (PH), and stalk diameter (SD). Two ML models were used: multiple linear regression (MLR) and random forest (RF). The results showed that models for predicting sugarcane NT, PH, and SD using time series and ML algorithms had accurate and precise predictions. Blue, Green, and NIR spectral bands provided the best performance in predicting sugarcane biometric attributes. These findings expand the possibilities for using multispectral UAV imagery in predicting sugarcane yield, particularly by including biophysical parameters.

Enhanced healing of oral chemical burn by inhibiting inflammatory factors with an oral administration of shengFu oil.

ShengFu oil is a compounded Chinese medicinal prescription, and provides antibacterial, anti-inflammatory, and analgesic effects, favoring burn wound repair. In this study, we aimed at investigating the effects of topical applications of ShengFu oil and its active ingredients in oral chemical burns and elucidating its regulatory effects on β-catenin, COX-2, and MMP-9 expression caused by exposure to acid or alkaline agents. ShengFu oil contains 16 components, such as Frankincense, Radix Scutellariae and Radix Rehmanniae, and the main active ingredients from Frankincense are α-pinene, linalool, and n-octanol. Mouse models of oral chemical burns were induced by using glacial acetic acid or sodium hydroxide. Hematoxylin and eosin staining and immunohistochemical staining were used to detect the protein expressions of β-catenin, COX-2, and MMP-9 in wound tissues. They were further quantified by multispectral imaging analysis to clarify the effective mechanism of ShengFu oil for intervening inflammatory factors and active components. Our results indicated that the application of ShengFu oil on oral chemical burns effectively stopped the oral burn bleeding and reduced the inflammatory reaction in the damaged tissues, demonstrating that ShengFu oil can promote wound tissue repair in burns caused by heat, acids, and alkalis. The immunohistochemical staining results illustrated that ShengFu oil and its active ingredients significantly reversed the abnormal changes in inflammation-related proteins in mouse tongue tissues that were caused by chemical burns. Regarding long-term toxic effects of ShengFu oil on the gastrointestinal tract, liver, and kidney system, the results of hematoxylin and eosin staining experiments depicted that ShengFu oil was safe and effective for liver, kidney, intestine, esophagus, and tongue. All of these demonstrated that ShengFu oil and its active ingredients are effective and safe in preventing and treating oral chemical burns by interfering with the inflammatory microenvironment.

FRI190 - Multispectral imaging analysis of liver biopsies from patients with non-alcoholic steatohepatitis reveals enrichment of pathogenic macrophage phenotypes associated with disease progression

FRI190 - Multispectral imaging analysis of liver biopsies from patients with non-alcoholic steatohepatitis reveals enrichment of pathogenic macrophage phenotypes associated with disease progression

Detecting hydrocarbon micro-seepage and related contamination, probable prospect areas, deduced from a comparative analysis of multispectral and hyperspectral satellite images

Detecting mineralogical alteration by hydrocarbon micro-seepage from trap to surface across the deformed structure, through Comparative analysis study of hyperspectral image (EO-1, Hyperion), and multispectral (landsat7 and Advanced land imager (Ali) to map soil alteration by petroleum seepage with applying remote sensing techniques as (band ratios, supervised, SAM classification, and hydrocarbon detection and index (HD and HI) calculation to detect the potentiality of hydrocarbon seepage in the area. The type of spectral resolution accuracy available for each space imaging platform can be used to choose which is best in determining the target and whether exploration yields oil. Hyperspectral remote sensing data are integrated with a GIS framework Weighted Sum (Spatial Analyst) classified hydrocarbon seepage prospects into five potential zones very good, good, intermediate, poor, and very poor probable potentiality. The spatial lithologic carbonaceous alteration appears closely coincident along reactivated leaking faults that cause hydrocarbon micro-seepage and altered surface lithology and mineralogical cement. The hyperspectral images have proven their worth in identifying and studying hydrocarbon leaks and the resulting environmental pollution and mineral alteration, as well as use micro-seeps as a pathfinder to locations of new oil explorations discoveries that can use as a pre-drill prediction of hydrocarbon occurrence and detected a prospect area for hydrocarbon drill.

Identification of super-exhausted T cells: A novel population predictive of response to immunotherapy.

2596 Background: Given that most of cancer patients treated with anti-PD1/PD-L1 immune checkpoint blockers (ICB) do not derive benefit, there is a crucial need to identify reliable predictive biomarker of response. Besides PD-1, several key immune checkpoints, such as CTLA4, LAG3, TIM3 and TIGIT, are associated with a T cell exhausted phenotype and play a crucial role in leading to cancer immune evasion. The impact of simultaneous expression by T cells of distinct inhibitory receptors on outcome of patients treated with ICB is still unknown. Methods: We analyzed the tissue samples, collected before ICB initiation, from patients with solid tumors and included in an institutional molecular profiling program (NCT02534649). We used multiplexed-immunohistofluorescence with the following panel CD3/PD1/TIM3/LAG3/TIGIT/CTLA4, and performed immune cell characterization using multispectral images analysis. We then investigated the correlation between coexpression of T cell-associated exhaustion markers, clinical response rate, progression-free survival (PFS) and overall survival (OS) by Cox proportional hazards models. Results: Four hundred thirty five patients were included in the analysis (NSCLC: n=207, 47.6%; sarcoma: n=42, 9.7%; urothelial: n=30, 6.9%; others: n=156, 35.9%). Digital pathology analysis allowed us to identify a population of “super-exhausted” T cells characterized by the co-expression of PD1, LAG3, TIGIT and TIM3 which was enriched in 125 cases (28.7%), and was significantly associated with better PFS (HR 1.60, CI95 1.26-2.04, p<0.001) and OS (HR 1.42, CI95 1.07-1.89, p=0.016) in the whole cohort. Patients with super-exhausted high tumors had higher objective response rate (38.4%) compared to super-exhausted low tumors (19.7%, p<0.001). The presence of super-exhausted T cells was significantly higher in responders (10%) versus non responders (4%, p<0.001). Correlation with better outcome was observed whatever the subgroup considered (NSCLC vs other tumors, CD8 T cells density and presence of tertiary lymphoid structure [TLS]). In multivariate analysis (n=372, 85.5%), increased tumor infiltration by super-exhausted T cells (>1 %) was significantly associated with better PFS (HR 0.61, CI95 0.46-0.81, p<0.001, Table) and OS (HR 0.68, CI95 0.48-0.97, p=0.033, Table). Conclusions: The presence of super-exhausted T cells may represent a new predictive biomarker of response to ICB and pave the way for the development of effective ICB combinations. Data from an independent validation cohort will be presented at the meeting. [Table: see text]

The use of multispectral imaging for the discrimination of Arabica and Robusta coffee beans.

Arabica coffee beans are sold at twice the price, or more, compared to Robusta beans and consequently are susceptible to economically motivated adulteration by substitution. There is a need for rapid, non-destructive, and efficient analytical techniques for monitoring the authenticity of Arabica coffee beans in the supply chain. In this study, multispectral imaging (MSI) was applied to discriminate roasted Arabica and Robusta coffee beans and perform quantitative prediction of Arabica coffee bean adulteration with Robusta. The Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model, built using selected spectral and morphological features from individual coffee beans, achieved 100% correct classification of the two coffee species in the test dataset. The OPLS regression model was able to successfully predict the level of adulteration of Arabica with Robusta. MSI analysis has potential as a rapid screening tool for the detection of fraud issues related to the authenticity of Arabica coffee beans.

Quantifying and visualizing 32 years of agricultural land use change in Kabul, Afghanistan

ABSTRACT Agriculture is a key element of Afghanistan’s economy and plays an essential role supporting the expanding population and urban development of Kabul, the country’s capital. Over the past decades the urban landscape has changed substantially and agricultural land use has shifted in its extent, location, and density. Identifying trends in the amount of agricultural area, as an indication of food production, is important for city planning and humanitarian efforts. While many studies have investigated Afghanistan's agriculture, most are conducted at scales that preclude their use for local-scale decision-making. This study quantifies agricultural extent across 32 years from 1988 to 2020 at local scale using simple and repeatable Landsat multispectral image analysis. The volume of data in time-series analysis complicatesvisualization of key findings and long-term trends. This study also explored visualization methods such as zonal mapping, animations, and the isolation of key themes in a 2D static map.

Non-Destructive Testing of Alfalfa Seed Vigor Based on Multispectral Imaging Technology.

Seed vigor is an important index to evaluate seed quality in plant species. How to evaluate seed vigor quickly and accurately has always been a serious problem in the seed research field. As a new physical testing method, multispectral technology has many advantages such as high sensitivity and accuracy, nondestructive and rapid application having advantageous prospects in seed quality evaluation. In this study, the morphological and spectral information of 19 wavelengths (365, 405, 430, 450, 470, 490, 515, 540, 570, 590, 630, 645, 660, 690, 780, 850, 880, 940, 970 nm) of alfalfa seeds with different level of maturity and different harvest periods (years), representing different vigor levels and age of seed, were collected by using multispectral imaging. Five multivariate analysis methods including principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) were used to distinguish and predict their vigor. The results showed that LDA model had the best effect, with an average accuracy of 92.9% for seed samples of different maturity and 97.8% for seed samples of different harvest years, and the average sensitivity, specificity and precision of LDA model could reach more than 90%. The average accuracy of nCDA in identifying dead seeds with no vigor reached 93.3%. In identifying the seeds with high vigor and predicting the germination percentage of alfalfa seeds, it could reach 95.7%. In summary, the use of Multispectral Imaging and multivariate analysis in this experiment can accurately evaluate and predict the seed vigor, seed viability and seed germination percentages of alfalfa, providing important technical methods and ideas for rapid non-destructive testing of seed quality.

Fluorescence spectroscopy and multispectral imaging for fingerprinting of aflatoxin-B1 contaminated (Zea mays L.) seeds: a preliminary study

Cereal seeds safety may be compromised by the presence of toxic contaminants, such as aflatoxins. Besides being carcinogenic, they have other adverse health effects on humans and animals. In this preliminary study, we used two non-invasive optical techniques, optical fiber fluorescence spectroscopy and multispectral imaging (MSI), for discrimination of maize seeds naturally contaminated with aflatoxin B1 (AFB1) from the uncontaminated seeds. The AFB1-contaminated seeds exhibited a red shift of the emission maximum position compared to the control samples. Using linear discrimination analysis to analyse fluorescence data, classification accuracy of 100% was obtained to discriminate uncontaminated and AFB1-contaminated seeds. The MSI analysis combined with a normalized canonical discriminant analysis, provided spectral and spatial patterns of the analysed seeds. The AFB1-contaminated seeds showed a 7.9 to 9.6-fold increase in the seed reflectance in the VIS region, and 10.4 and 12.2-fold increase in the NIR spectral region, compared with the uncontaminated seeds. Thus the MSI method classified successfully contaminated from uncontaminated seeds with high accuracy. The results may have an impact on development of spectroscopic non-invasive methods for detection of AFs presence in seeds, providing valuable information for the assessment of seed adulteration in the field of food forensics and food safety.

A Method for Object Classification in Aerial/Satellite Images with Incorporating Geospatial Information

Aerial and satellite multispectral images are important source of intelligence information. However, the object classification accuracy in those images for reasons such as camouflage, use of decoys, and others often turns out to be insufficient. The objective of the study is to develop a method for computer-aided analysis of aerial and satellite multispectral images, which allows improving classification accuracy. This objective is achieved by incorporating geospatial information (topographic, geodetic, about land cover types) into the classification process. As a mathematical basis of the method is used subjective logic of A. Jøsang. The effectiveness of the proposed method has been demonstrated by computer modeling using ArcGIS ModelBuilder tools.

A Method for Object Classification in Aerial/Satellite Images with Incorporating Geospatial Information

Aerial and satellite multispectral images are important source of intelligence information. However, the object classification accuracy in those images for reasons such as camouflage, use of decoys, and others often turns out to be insufficient. The objective of the study is to develop a method for computer-aided analysis of aerial and satellite multispectral images, which allows improving classification accuracy. This objective is achieved by incorporating geospatial information (topographic, geodetic, about land cover types) into the classification process. As a mathematical basis of the method is used subjective logic of A. Jøsang. The effectiveness of the proposed method has been demonstrated by computer modeling using ArcGIS ModelBuilder tools.

Fenotipagem de alto rendimento por análise de imagens RGB e multiespectral de genótipos em milho doce

ABSTRACT Sweet corn (Zea mays subsp. saccharata) is mainly intended for industrial processing. Optimizing time and costs during plant breeding is fundamental. An alternative is the use of high-throughput phenotyping (HTP) indirect associated with agronomic traits and chlorophyll contents. This study aimed to (i) verify whether HTP by digital images is useful for screening sweet corn genotypes and (ii) investigate the correlations between the traits evaluated by conventional methods and those obtained from images. Ten traits were evaluated in seven S3 populations of sweet corn and in two commercial hybrids, three traits by classical phenotyping and the others by HTP based on RGB (red, green, blue) and multispectral imaging analysis. The data were submitted to the analyses of variance and Scott-Knott test. In addition, a phenotypic correlation graph was plotted. The hybrids were more productive than the S3 populations, showing an efficient evaluation. The traits extracted using HTP and classical phenotyping showed a high degree of association. HTP was efficient in identifying sweet corn genotypes with higher and lower yield. The vegetative canopy area (VCA), normalized difference vegetation index (NDVI), and visible atmospherically resistant index (VARI) indices were strongly associated with grain yield.

Quantification of chlorophyll fluorescence in soybean seeds by multispectral images and their relationship with physiological potential

Abstract: The multispectral image analysis technique to detect chlorophyll fluorescence (CF) in soybean seeds was studied to assess the relationship between CF signals and seed physiological potential. Eight treatments, corresponding to 0%, 2%, 4%, 6%, 8%, 10%, 12%, and 14% green seeds, were used on two cultivars, BMX Desafio RR 8473 RSF and 96R10 IPRO, which passed through different seed quality tests. Initially, the CF of the seeds was determined using 660 nm and 730 nm spectra, and then the germination, electrical conductivity, accelerated aging with saturated NaCl solution, tetrazolium, and computerized seedling image analysis (Vigor-S) tests were performed on the same seeds. A completely randomized design was used, as well as replications of each treatment. Analysis of variance (ANOVA) was performed on the data from germination, vigor, and CF tests using the R® software, and the means were grouped by the Scott-Knott test (p ≤ 0.05). Pearson’s linear correlation coefficients (r) were calculated for all combinations among the evaluations with significance of the r values determined by the t-test (p ≤ 0.05), and multivariate analysis of the principal components was performed. Proportional increases in green seeds contribute to an increase in chlorophyll fluorescence signals and have a negative correlation with seed physiological quality; levels above 4% green seeds in the samples result in marked losses in physiological potential. Therefore, the chlorophyll fluorescence detected through multispectral images is inversely related to the physiological potential of soybean seeds.

Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery.

Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.

Structural Control of Gold-Ore Mineralization for the Eastern Slope of the Polar Urals (Russia) Based on Analysis of Multispectral Images of the Landsat 8 Spacecraft

Structural Control of Gold-Ore Mineralization for the Eastern Slope of the Polar Urals (Russia) Based on Analysis of Multispectral Images of the Landsat 8 Spacecraft

Nutritional status assessment of olive crops by means of the analysis and modelling of multispectral images taken with UAVs

This research was aimed at developing an efficient method for Nitrogen, Phosphorus, and Potassium (NPK) foliar content retrieval in olive trees by means of the analysis and modelling multispectral images taken by an unmanned aerial vehicle (UAV) under field conditions. To this end, an experiment was carried out in a super hight density olive orchard. The fertirrigation system of the experimental area was sectorized to obtain plots with different status of NPK. The orchard was overflown with a UAV equipped with a multispectral camera that photographed the entire experimental surface. A new image analysis approach was developed for integrating all the spectral images gathered during the flight in orthomosaics from which to automatically extract information from discrete points. Finally, several retrieval techniques (partial least squares regression, artificial neural network (ANN), support vector regression and Gaussian process regression) were evaluated for NPK leaf content retrieval by using the spectral data as input variables, and the results of chemical analyses as reference. Among all, the best results were obtained by ANN approach (N (R 2 = 0.63), P (R 2 = 0.89), K (R 2 = 0.93)). These results showed the suitability of the proposed image processing approach and indicate ANN as the best recovery technique for the experimental conditions evaluated. However, the approach must be validated under other environmental conditions, olive varieties and plant vegetative stages before making fertilization recommendations. • Method for NPK leaf content retrieval in olive trees by means of multispectral data. • Multispectral images taken by a UAV under field conditions. • Image analysis algorithm to automatically extract information from discrete points. • ANN showed better results than PLSR, SVR and GPR.

Integration of multispectral imaging, XRF mapping and Raman analysis for noninvasive study of illustrated manuscripts: the case study of fifteenth century “Humay meets the Princess Humayun” Persian masterpiece from Louvre Museum

Integration of multispectral imaging, XRF mapping and Raman analysis for noninvasive study of illustrated manuscripts: the case study of fifteenth century “Humay meets the Princess Humayun” Persian masterpiece from Louvre Museum