Adsorption modeling of thin-film molecularly imprinted polymers to measure pyrene in marine environments

This thesis characterizes the adsorption properties of molecularly imprinted polymers (MIPs) for pyrene. MIPs are applied for monitoring organic pollutants such as polycyclic aromatic hydrocarbons (PAHs) and can be deployed in freshwater and marine (saline) water for detection of anthropogenic impacts. Exploiting a simple, low-tech method is of high priority as the fast detection of oil spill sources has become a challenge to analytical chemists and environmental scientists. Kinetic tests were conducted employing the developed MIPs in our group using an Ultra Performance Liquid Chromatography (UPLC) for the measurement of pyrene, PAHs, in both small and large volume studies. Discussed will be the results obtained from kinetic and equilibrium adsorption experiments conducted using these films for measuring pyrene in distilled, tap water, and saline water. To gain an insight into the effects of temperature, pH, and salinity, the same experiments were conducted at varying temperature, pH, and salinity employing the standard batch equilibrium method. This research evaluates the adsorption capacity of the obtained polymers as a means to use MIPs for onsite detection without elaborate calibration making them suitable for use in early warning systems for oil spills

Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements

© 2014 by the authors. Spectral Vegetation Indices (SVIs) have been widely used to indirectly detect plant diseases. The aim of this research is to evaluate the effect of different disease symptoms on SVIs and introduce suitable SVIs to detect rust disease. Wheat leaf rust is one of the prevalent diseases and has different symptoms including yellow, orange, dark brown, and dry areas. The reflectance spectrum data for healthy and infected leaves were collected using a spectroradiometer in the 450 to 1000 nm range. The ratio of the disease-affected area to the total leaf area and the proportion of each disease symptoms were obtained using RGB digital images. As the disease severity increases, so does the scattering of all SVI values. The indices were categorized into three groups based on their accuracies in disease detection. A few SVIs showed an accuracy of more than 60% in classification. In the first group, NBNDVI, NDVI, PRI, GI, and RVSI showed the highest amount of classification accuracy. The second and third groups showed classification accuracies of about 20% and 40% respectively. Results show that few indices have the ability to indirectly detect plant disease

Developing two spectral disease indices for detection of wheat leaf rust (Pucciniatriticina)

Spectral vegetation indices (SVIs) have been widely used to detect different plant diseases. Wheat leaf rust manifests itself as an early symptom with the leaves turning yellow and orange. The sign of advancing disease is the leaf colour changing to brown while the final symptom is when the leaf becomes dry. The goal of this work is to develop spectral disease indices for the detection of leaf rust. The reflectance spectra of the wheat's infected and non-infected leaves at different disease stages were collected using a spectroradiometer. As ground truth, the ratio of the disease-affected area to the total leaf area and the fractions of the different symptoms were extracted using an RGB digital camera. Fractions of the various disease symptoms extracted by the digital camera and the measured reflectance spectra of the infected leaves were used as input to the spectral mixture analysis (SMA). Then, the spectral reflectance of the different disease symptoms were estimated using SMA and the least squares method. The reflectance of different disease symptoms in the 450~1000 nm were studied carefully using the Fisher function. Two spectral disease indices were developed based on the reflectance at the 605, 695 and 455 nm wavelengths. In both indices, the R2 between the estimated and the observed was as highas 0.94. © 2014 by the authors; licensee MDPI, Basel, Switzerland

Hyperspectral canopy sensing of wheat septoria tritici blotch disease

Producing quantitative and reliable measures of crop disease is essential for resistance breeding, but is challenging and time consuming using traditional phenotyping methods. Hyperspectral remote sensing has shown potential for the detection of plant diseases, but its utility for phenotyping large and diverse populations of plants under field conditions requires further evaluation. In this study, we collected canopy hyperspectral data from 335 wheat varieties using a spectroradiometer, and we investigated the use of canopy reflectance for detecting the Septoria tritici blotch (STB) disease and for quantifying the severity of infection. Canopy- and leaf-level infection metrics of STB based on traditional visual assessments and automated analyses of leaf images were used as ground truth data. Results showed (i) that canopy reflectance and the selected spectral indices show promise for quantifying STB infections, and (ii) that the normalized difference water index (NDWI) showed the best performance in detecting STB compared to other spectral indices. Moreover, partial least squares (PLS) regression models allowed for an improvement in the prediction of STB metrics. The PLS discriminant analysis (PLSDA) model calibrated based on the spectral data of four reference varieties was able to discriminate between the diseased and healthy canopies among the 335 varieties with an accuracy of 93% (Kappa = 0.60). Finally, the PLSDA model predictions allowed for the identification of wheat genotypes that are potentially more susceptible to STB, which was confirmed by the STB visual assessment. This study demonstrates the great potential of using canopy hyperspectral remote sensing to improve foliar disease assessment and to facilitate plant breeding for disease resistance

Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress

This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into ‘healthy and diseased plant classification’ with an emphasis on classification accuracy, early detection of stress, and disease severity. A central focus of the review is the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease. A summary of techniques used to detect biotic and abiotic stress in plants is presented, including the level of accuracy associated with each method

Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery

This work is focused on the spatio-temporal monitoring of winter wheat inoculated with various levels of yellow rust inoculum during the entire growth season. A dedicated work ow is devised to obtain time-series five-bands (visible-infrared) aerial imageries with a multispectral camera and an Unmanned Aerial Vehicle. A number of spectral indices are drawn so that the sensitive ones can be identi fied by statistical dependency analysis; particularly, their discriminating capabilities are evaluated at diffeerent stages for both wheat pixel segmentation and yellow rust severity. Then the spatial-temporal changes of sensitive bands/indices are evaluated and analysed quantitatively. A validation fi eld experiment was designed in 2017-2018 by inoculating wheat with one of the six levels of yellow rust inoculum. Five-bands RedEdge camera on-board DJI S1000 was used to capture aerial images at eight time points covering the entire growth season at an altitude of about 20 meters with a ground resolution of 1-1.5 cm/pixel. Experimental results via spatio-temporal analysis show that: (1) various bands/indices should be used for wheat segmentation at different stages; (2) no bands/indices differences are observed for yellow rust inoculated wheat plots in both incubation stage (9 days after inoculation) and early onset stage (25 days after inoculation); (3) NIR and Red are the sensitive bands for wheat yellow rust in disease stages (45 days after inoculation); and their normalized difference NDVI index provides an even higher statistical dependency; (4) bands/indices' sensitivity to yellow rust changes over time and decreases in later Heading stage until being very low in Ripening stage (61 days after inoculation). This experimental study provides a crucial guidance for future early spatio-temporal yellow rust monitoring at farmland scales

In-field detection and quantification of Septoria tritici blotch in diverse wheat germplasm using spectral-temporal features

Hyperspectral remote sensing holds the potential to detect and quantify crop diseases in a rapid and non-invasive manner. Such tools could greatly benefit resistance breeding, but their adoption is hampered by i) a lack of specificity to disease-related effects and ii) insufficient robustness to variation in reflectance caused by genotypic diversity and varying environmental conditions, which are fundamental elements of resistance breeding. We hypothesized that relying exclusively on temporal changes in canopy reflectance during pathogenesis may allow to specifically detect and quantify crop diseases whilst minimizing the confounding effects of genotype and environment. To test this hypothesis, we collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral-temporal features representing this hypothesis. Our results confirm the lack of specificity and robustness of disease assessments based on reflectance spectra at individual time points. We show that changes in spectral reflectance over time are indicative of the presence and severity of septoria tritici blotch (STB) infections. Furthermore, the proposed time-integrated approach facilitated the delineation of disease from physiological senescence, which is pivotal for efficient selection of STB-resistant material under field conditions. A validation of models based on spectral-temporal features on a diverse panel of >300 wheat genotypes offered evidence for the robustness of the proposed method. This study demonstrates the potential of time-resolved canopy reflectance measurements for robust assessments of foliar diseases in the context of resistance breeding

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Developing Two Spectral Disease Indices for Detection of Wheat Leaf Rust (Pucciniatriticina)

Spectral vegetation indices (SVIs) have been widely used to detect different plant diseases. Wheat leaf rust manifests itself as an early symptom with the leaves turning yellow and orange. The sign of advancing disease is the leaf colour changing to brown while the final symptom is when the leaf becomes dry. The goal of this work is to develop spectral disease indices for the detection of leaf rust. The reflectance spectra of the wheat’s infected and non-infected leaves at different disease stages were collected using a spectroradiometer. As ground truth, the ratio of the disease-affected area to the total leaf area and the fractions of the different symptoms were extracted using an RGB digital camera. Fractions of the various disease symptoms extracted by the digital camera and the measured reflectance spectra of the infected leaves were used as input to the spectral mixture analysis (SMA). Then, the spectral reflectance of the different disease symptoms were estimated using SMA and the least squares method. The reflectance of different disease symptoms in the 450~1000 nm were studied carefully using the Fisher function. Two spectral disease indices were developed based on the reflectance at the 605, 695 and 455 nm wavelengths. In both indices, the R2 between the estimated and the observed was as highas 0.94

Real-time Model and Simulation Architecture for Half- and Full-bridge Modular Multilevel Converters

This work presents an equivalent model and simulation architecture for real-time electromagnetic transient analysis of either half-bridge or full-bridge modular multilevel converter (MMC) with 400 sub-modules (SMs) per arm. The proposed CPU/FPGA-based architecture is optimized for the parallel implementation of the presented MMC model on the FPGA and is beneficiary of a high-throughput floating-point computational engine. The developed real-time simulation architecture is capable of simulating MMCs with 400 SMs per arm at 825 nanoseconds. To address the difficulties of the sorting process implementation, a modified Odd-Even Bubble sorting is presented in this work. The comparison of the results under various test scenarios reveals that the proposed real-time simulator is representing the system responses in the same way of its corresponding off-line counterpart obtained from the PSCAD/EMTDC program.M.A.S