Query: hyperspectral electron imaging

Candidate papers:

abstract: ABSTRACT Materials engineers have increasing control over nanoscale chemical composition and nanostructures. The latter can be easily analysed by the scanning electron microscope (SEM) yet nanoscale chemical analysis in the SEM poses challenges. Nevertheless, nanoscale chemical analysis capabilities have been developed in the LV SEM by secondary electron hyperspectral imaging (SEHI) In this article, the insights that SEHI has provided into well characterised semiconductor, photovoltaic, polymer and hard carbon materials are reviewed. Instrument and experimental considerations for the obtainment of secondary electron hyper spectra are discussed and recommendations for future analyses in the LV SEM by SEHI are made.
author_names: James F Nohl
title: Secondary electron hyperspectral imaging: nanostructure and chemical analysis for the LV SEM
year: 2020

abstract: Digital plant phenotyping is emerging as a key research domain at the interface of information technology and plant science. Digital phenotyping aims to deploy high end non destructive sensing techniques and information technology infrastructures to automate the extraction of both structural and physiological traits from plants under phenotyping experiments. One of the promising sensor technologies for plant phenotyping is hyperspectral imaging (HSI) The main benefit of utilising HSI compared to other imaging techniques is the possibility to extract simultaneously structural and physiological information on plants. The use of HSI for analysis of parts of plants, e.g. plucked leaves, has already been demonstrated. However, there are several significant challenges associated with the use of HSI for extraction of information from a whole plant, and hence this is an active area of research. These challenges are related to data processing after image acquisition. The hyperspectral data acquired of a plant suffers from variations in illumination owing to light scattering, shadowing of plant parts, multiple scattering and a complex combination of scattering and shadowing. The extent of these effects depends on the type of plants and their complex geometry. A range of approaches has been introduced to deal with these effects, however, no concrete approach is yet ready. In this article, we provide a comprehensive review of recent studies of close range HSI of whole plants. Several studies have used HSI for plant analysis but were limited to imaging of leaves, which is considerably more straightforward than imaging of the whole plant, and thus do not relate to digital phenotyping. In this article, we discuss and compare the approaches used to deal with the effects of variation in illumination, which are an issue for imaging of whole plants. Furthermore, future possibilities to deal with these effects are also highlighted.
author_names: Puneet Mishra

Santosh Lohumi

Haris Ahmad Khan

Alison Nordon
title: Close range hyperspectral imaging of whole plants for digital phenotyping: Recent applications and illumination correction approaches
venue: Comput. Electron. Agric.
year: 2020

abstract: Abstract Understanding nanoscale molecular order within organic electronic materials is a crucial factor in building better organic electronic devices. At present, techniques capable of imaging molecular order within a polymer are limited in resolution, accuracy, and accessibility. In this work, presented are secondary electron (SE) spectroscopy and secondary electron hyperspectral imaging, which make an exciting alternative approach to probing molecular ordering in poly(3 hexylthiophene) (P3HT) with scanning electron microscope enabled resolution. It is demonstrated that the crystalline content of a P3HT film is reflected by its SE energy spectrum, both empirically and through correlation with nano Fourier transform infrared spectroscopy, an innovative technique for exploring nanoscale chemistry. The origin of SE spectral features is investigated using both experimental and modeling approaches, and it is found that the different electronic properties of amorphous and crystalline P3HT result in SE emission with different energy distributions. This effect is exploited by acquiring hyperspectral SE images of different P3HT films to explore localized molecular orientation. Machine learning techniques are used to accurately identify and map the crystalline content of the film, demonstrating the power of an exciting characterization technique.
author_names: Robert C Masters

Nicola Stehling

Kerry J Abrams

Vikas Kumar

Martina Azzolini

Nicola Maria Pugno

Maurizio Dapor

Andreas J Huber

Philip Schafer

David G Lidzey

Cornelia Rodenburg
title: Mapping Polymer Molecular Order in the SEM with Secondary Electron Hyperspectral Imaging
venue: Advanced science
year: 2019

abstract: Abstract Carbon and carbon/metal systems with a multitude of functionalities are ubiquitous in new technologies but understanding on the nanoscale remains elusive due to their affinity for interaction with their environment and limitations in available characterization techniques. This paper introduces a spectroscopic technique and demonstrates its capacity to reveal chemical variations of carbon. The effectiveness of this approach is validated experimentally through spatially averaging spectroscopic techniques and using Monte Carlo modeling. Characteristic spectra shapes and peak positions for varying contributions of sp2 like or sp3 like bond types and amorphous hydrogenated carbon are reported under circumstances which might be observed on highly oriented pyrolytic graphite (HOPG) surfaces as a result of air or electron beam exposure. The spectral features identified above are then used to identify the different forms of carbon present within the metallic films deposited from reactive organometallic inks. While spectra for metals is obtained in dedicated surface science instrumentation, the complex relations between carbon and metal species is only revealed by secondary electron (SE) spectroscopy and SE hyperspectral imaging obtained in a state of the art scanning electron microscope (SEM) This work reveals the inhomogeneous incorporation of carbon on the nanoscale but also uncovers a link between local orientation of metallic components and carbon form.
author_names: Kerry J Abrams

Maurizio Dapor

Nicola Stehling

Martina Azzolini

Stephan J Kyle

Jan Schafer

Antje Quade

Filip Mika

Stanislav Kratky

Zuzana Pokorna

Ivo Konvalina

Danielle Mehta

Kate Black

Cornelia Rodenburg
title: Making Sense of Complex Carbon and Metal/Carbon Systems by Secondary Electron Hyperspectral Imaging
venue: Advanced science
year: 2019

abstract: A novel capability built upon secondary electron (SE) spectroscopy provides an enhanced cross linking characterization toolset for polymeric biomaterials, with cross linking density and variation captured at a multiscale level. The potential of SE spectroscopy for material characterization has been investigated since 1947. The absence of suitable instrumentation and signal processing proved insurmountable barriers to applying SE spectroscopy to biomaterials, and consequently, capturing SE spectra containing cross linking information is a new concept. To date, cross linking extent is inferred from analytical techniques such as nuclear magnetic resonance (NMR) differential scanning calorimetry, and Raman spectroscopy (RS) NMR provides extremely localized information on the atomic scale and molecular scale, while RS information volume is on the microscale. Other methods for the indirect study of cross linking are bulk mechanical averaging methods, such as tensile and compression modulus testing. However, these established averaging methods for the estimation of polymer cross linking density are incomplete because they fail to provide information of spatial distributions within the biomaterial morphology across all relevant length scales. The efficacy of the SE spectroscopy capability is demonstrated in this paper by the analysis of poly(glycerol sebacate) methacrylate (PGS M) at different degrees of methacrylation delivering new insights into PGS M morphology.
author_names: Nicholas Farr

Samand Pashneh-Tala

Nicola Stehling

Frederik Claeyssens

Nicola H Green

Cornelia Rodenburg
title: Characterizing Cross Linking Within Polymeric Biomaterials in the SEM by Secondary Electron Hyperspectral Imaging.
venue: Macromolecular rapid communications
year: 2019

abstract: Abstract High throughput phenotyping technologies in controlled environments or field conditions have proven to be extremely useful in unravelling key quantitative traits of plants for breeding. Among many plant phenotyping methods, hyperspectral imaging (HSI) and three dimensional (3D) sensing are the fastest growing and promising approaches for measuring multiple plant parameters. There are many types of HSI and 3D sensors available with each being designed for a specific purpose. Also, the same sensor could be set up and calibrated in different ways to measure different plant parameters on various platforms. This review aims to guide the use of HSI and 3D sensing technologies for plant phenotyping. It first introduces the preliminary knowledge of HSI and 3D sensing for plant phenotyping. In addition, it provides the detail of plant phenotyping using different HSI and 3D sensors on various platforms with different scales. Lastly, the problems and challenges of close range HSI and 3D modelling of plants are discussed and potential solutions are suggested.
author_names: Huajian Liu

Brooke Bruning

Trevor Garnett

Bettina Berger
title: Hyperspectral imaging and 3D technologies for plant phenotyping: From satellite to close range sensing
venue: Comput. Electron. Agric.
year: 2020

abstract: Abstract A comprehensive study of the feasibility of hyperspectral imaging in visible (400 1000 nm) and near infrared (900 1700 nm) regions was investigated for prediction and concentration mapping of Vitamin C, ascorbic acid (AA) dehydroascorbic acid (DHAA) and phenols in wild rocket (Diplotaxis tenuifolia) over a storage span of 12 days at 5 degC. Partial least squares regression (PLSR) with different data pretreatments and wavelength selection resulted in satisfactory predictions for all parameters in the NIR range except DHAA. Prediction models were used for concentration mapping to follow changes over time. The prediction maps will be comprehensively study to assess the pixel to pixel variation within the rocket leaves. The PLSR models for Vitamin C, AA and phenols yielded an R2 of 0.76, 0.73 and 0.78, respectively in external prediction with root mean square errors approximately equivalent to those of reference analysis. Conclusively, hyperspectral imaging, with the correct mapping approach, can be a useful tool for the prediction and mapping of phytonutrients in wild rocket (Diplotaxis tenuifolia) over time.
author_names: Muahmmad M A Chaudhry

Maria Luisa Amodio

Jose Manuel Amigo

Maria L V de Chiara

Farahmand Babellahi

Giancarlo Colelli
title: Feasibility study for the surface prediction and mapping of phytonutrients in minimally processed rocket leaves (Diplotaxis tenuifolia) during storage by hyperspectral imaging
venue: Comput. Electron. Agric.
year: 2020

abstract: Abstract This research was conducted to examine the potential use of hyperspectral imaging for non destructive detection of Grapevine leafroll associated virus 3 (GLRaV 3) during asymptomatic and symptomatic stages of grapevine leafroll disease (GLD) in a red berried wine grape (Vitis vinifera) cultivar. Cabernet Sauvignon vines tested positive and negative for GLRaV 3 were used in this study. Leaves from infected and non infected vines were detached at five phenological stages and individual leaf images were acquired by a hyperspectral imager in 2017, 2018, and 2019 seasons. Those images were then preprocessed using spectra normalization and Monte Carlo method for eliminating spectral sample outliers. Least absolute shrinkage and selection operator was used to select feature wavelengths for each phenological stage within three season datasets. The sensitivity of selected feature wavelengths was evaluated based on analysis of variance and linear regression. Six salient wavelengths (690, 715, 731, 1409, 1425 and 1582 nm) were determined as sensitive wavebands for detecting virus symptoms in leaf samples. The detectability of GLD using those six salient wavelengths was evaluated using least squares support vector machine. The classification accuracy was found between 66.67 and 89.93% for test datasets collected at first asymptomatic stage over three seasons. These results indicated that the hyperspectral imaging technique has the potential for nondestructively detecting virus infected grapevines during asymptomatic stages.
author_names: Zongmei Gao

Lav R Khot

Rayapati A Naidu

Qin Zhang
title: Early detection of grapevine leafroll disease in a red berried wine grape cultivar using hyperspectral imaging
venue: Comput. Electron. Agric.
year: 2020

abstract: Abstract Automatic detection of early blight caused by Alternaria solani could promote a drastic reduction in the consumption of plant protection agents and the related production losses. A proximal sensing platform was constructed and calibrated for acquiring high resolution hyperspectral images in the field, and used to accurately map Alternaria lesions. High resolution canopy reflectance images were obtained for 32 potato plants that had been infected with A. solani and 32 healthy reference plants. Spectral classifiers like partial least squares discriminant analysis (PLS DA) and support vector machines (SVM) based on PCA scores were tested to discriminate affected and non affected pixels. Both spectral classifiers performed well at pixel level with accuracies above 0.92. The NIR region (750 nm) was identified as the most discriminative part of the spectrum for detecting the lesions. As the disease pressure is typically expressed as the number of lesions per area, the accuracy was also evaluated at this level. This indicated a considerable number of false detections at the edges of the leaves and the leaf axils. Therefore, a decision tree was designed based on expert knowledge about the shape of Alternaria lesions, and used to post process the classified images. This reduced the number of false detections, increasing the precision from 0.17 to 0.22 at the expense of a reduction in recall from 0.88 to 0.84. This leaves considerable room for improvement in the classification accuracy at the object level. We learned that (1) few, broad wavelengths are sufficient and (2) spatial context is essential for the detection of lesions caused by Alternaria infection. The application of more powerful object classification techniques such as convolutional neural networks to enhance the model performance by efficiently encapsulating the spatial context in the classifier might further improve the detection performance. This could pave the way to UAV or tractor based Alternaria mapping.
author_names: Ruben Van de Vijver

Koen C Mertens

Kurt Heungens

Ben Somers

David Nuyttens

Irene Borra-Serrano

Peter Lootens

Isabel Roldan-Ruiz

Jurgen Vangeyte

Wouter Saeys
title: In field detection of Alternaria solani in potato crops using hyperspectral imaging
venue: Comput. Electron. Agric.
year: 2020

abstract: Abstract Crop identification and classification are the basis of fine agricultural development. Hyperspectral remote sensing has been utilized as a cost efficient approach. Hyperspectral remote sensing data sensing technology has achieved breakthroughs in modern technologies such as long term dynamic monitoring of crop growth, crop species damage, and acquisition the agricultural information accurately. The novelty of this paper appears in the simulation and prediction of some crops according to wavelength of bands. In the present study, the hyperspectral curves and sensitive bands of 400 samples for four varieties crops (Red medlar or wolfberry, jujube, watermelons and grape) were collected using a hyperspectral instrument. Some multivariate analyses were used for identifying optimal bands, simulation and prediction of different agricultural crops. Principal component analysis (PCA) load coefficient method (LCM) successive projection algorithm (SPA) partial least squares regression (PLS) and competitive adaptive reweighting sampling (CARS) methods were used to select characteristic wavelengths using Euclidean distance. High value of Euclidean distance was 15.13, 14.095, 16.305 and 14.10 between medlar jujube, jujube watermelons, jujube watermelons and jujube grape, respectively; it is appears high in CARS among other multivariate analysis. The results show that these characteristic bands can distinguish four kinds of characteristic crops well, but the effect of CARS extraction for selected crops is the best. For prediction of optimal bands, PLS was proven to be the most accurate model of crop prediction, a prediction was developed by applying the CARS PLS DA model based on each pixel at the optimal wavelengths. All data and points that selected for wolfberry prediction is very accurate not missing any points for its prediction; on the other hand, foe melon one point of wolfberry appear in the prediction of melon. For Jujube, some points from other crops interface in its prediction; on the other hand grape doesn't miss any points in its prediction. Prediction of selected crops shows that grape and watermelon have the best crops in specificity and sensitivity, respectively. Cross validation and calibration confirm the possibility of hyperspectral imagine for prediction of all crops especially grape and watermelon. Different multivariate analysis proved its ability to identify optimal bands of diffident species of crops under any conditions. The overall results indicated that hyperspectral imaging could be used for crop identification and prediction for effective classification technique.
author_names: Guan-lin Hong

Hazem T Abd El-Hamid
title: Hyperspectral imaging using multivariate analysis for simulation and prediction of agricultural crops in Ningxia, China
venue: Comput. Electron. Agric.
year: 2020