Spectral Preprocessing and Calibration Techniques

Yao, Haibo; Lewis, D. T.

doi:10.1016/b978-0-12-374753-2.10002-4

Cited by 31 publications

(22 citation statements)

References 64 publications

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“…The SG input parameters were tuned with the objective of achieving good correlation results. Also, the aforementioned methods were applied in order to remove and minimize any unwanted spectral contributions such as light scatter (Yao et al, 2010).…”

Section: Hyperspectral Images Preprocessingmentioning

confidence: 99%

On-line system based on hyperspectral information to estimate acidity, moisture and peroxides in olive oil samples

Gila

Marchal

García

et al. 2015

Computers and Electronics in Agriculture

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Section: Hyperspectral Images Preprocessingmentioning

confidence: 99%

On-line system based on hyperspectral information to estimate acidity, moisture and peroxides in olive oil samples

Gila

Marchal

García

et al. 2015

Computers and Electronics in Agriculture

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“…Radiance values were then converted to reflectance using the previously mentioned permanently mounted panels, for the subset of datasets where these were available. The calibrated reflectance is obtained by dividing by the reference panel at-sensor radiance and multiplying by its reflectance 37. The result is reflectance spectra that are invariant to changes in environmental illumination.More advanced methods of illumination compensation that do not require a continuous view of the reference panel are possible 15.…”

mentioning

confidence: 99%

Efficient in‐field plant phenomics for row‐crops with an autonomous ground vehicle

Underwood

Wendel

Schofield

et al. 2017

Journal of Field Robotics

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The scientific areas of plant genomics and phenomics are capable of improving plant productivity, yet they are limited by the manual labor that is currently required to perform in-field measurement, and a lack of technology for measuring the physical performance of crops growing in the field. A variety of sensor technology has the potential to efficiently measure plant characteristics that are related to production. Recent advances have also shown that autonomous airborne and manually driven ground-based sensor platforms provide practical mechanisms for deploying the sensors in the field. This paper advances the state-of-the-art by developing and rigorously testing an efficient system for high throughput in-field agricultural row-crop phenotyping. The system comprises an autonomous unmanned ground-vehicle robot for data acquisition and an efficient data post-processing framework to provide phenotype information over large-scale real-world plant-science trials. Experiments were performed at three trial locations at two different times of year, resulting in a total traversal of 43.8 km to scan 7.24 hectares and 2423 plots (including repeated scans). The height and canopy closure data were found to be highly repeatable (r 2 = 1.00 N = 280, r 2 = 0.99 N = 280, respectively) and accurate with respect to manually gathered field data (r 2 = 0.95 N = 470, r 2 = 0.91 N = 361, respectively), yet more objective and less-reliant on human skill and experience. The system was found to be a more labor-efficient mechanism for gathering data, which compares favorably to current standard manual practices. K E Y W O R D Sagriculture, hyperspectral and lidar sensing, plant phenomics, row-crop phenotyping, terrestrial robotics INTRODUCTIONPredicted global population increases are expected to cause a doubling in food demand by 2050, while at the same time the ability to grow more food is threatened by problems of water scarcity, soil fertility, and climate change. 12 Significant increases in food production are required, which will necessitate greater productivity in terms of yield per hectare and efficient use of natural resources. Given that "genetic diversity provides the basis for all plant improvement," 12 the study of different genetic varieties of crop (genomics) and how well they grow in different environmental conditions (phenomics) is critical to meet this challenge. Each year, around the world, millions of agricultural crops (such as grains and legumes) with different genetic profiles are grown in the field, subjected to different environmental factors (e.g., exposed to disease, herbicides, water stress, etc.) and the physical response of the plants (e.g., how tolerant they are, how much yield they produce) is measured. The process is repeated annually, driving plant productivity and adaptability forward, however, advances in genomics have not been matched by similar advances in phenomics and the ability to obtain these physical measurements is considered to be the major bottleneck. 2,12,14 Crop characteristics (phenotype t...

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“…Spectralon diffuse reflectance standard target is considered one of the common reference standards used in most of the spectroscopic applications for calibrations due to its flatness and constant reflectivity over the range 250-2500 nm 10,24 . However, due to the cost of such standards it is difficult to use a standard target of the same size as the scanner stage (A3 size), thus Whatman filter paper No.…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…Techniques such as hyperspectral imaging have combined imaging and material characterization 2 by expanding spectroscopic applications from spot analysis to the examination of the entire surface of an object which consequently provided valuable information about the distribution of material properties [3][4][5][6][7] . The spectral data collected especially in the near infrared region could be used to study the physical and chemical composition of objects [8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

Analytical robustness of quantitative NIR chemical imaging for Islamic paper characterization

et al. 2017

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Recently, spectral imaging techniques such as Multispectral (MSI) and Hyperspectral Imaging (HSI) have gained importance in the field of heritage conservation. This paper explores the analytical robustness of quantitative chemical imaging for Islamic paper characterization by focusing on the effect of different measurement and processing parameters, i.e. acquisition conditions and calibration on the accuracy of the collected spectral data. This will provide a better understanding of the technique that can provide a measure of change in collections through imaging. For the quantitative model, special calibration target was devised using 105 samples from a well-characterized reference Islamic paper collection. Two material properties were of interest: starch sizing and cellulose degree of polymerization (DP). Multivariate data analysis methods were used to develop discrimination and regression models which were used as an evaluation methodology for the metrology of quantitative NIR chemical imaging. Spectral data were collected using a pushbroom HSI scanner (Gilden Photonics Ltd) in the 1000-2500 nm range with a spectral resolution of 6.3 nm using a mirror scanning setup and halogen illumination. Data were acquired at different measurement conditions and acquisition parameters. Preliminary results showed the potential of the evaluation methodology to show that measurement parameters such as the use of different lenses and different scanning backgrounds may not have a great influence on the quantitative results. Moreover, the evaluation methodology allowed for the selection of the best pre-treatment method to be applied to the data.

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Spectral Preprocessing and Calibration Techniques

Cited by 31 publications

References 64 publications

On-line system based on hyperspectral information to estimate acidity, moisture and peroxides in olive oil samples

On-line system based on hyperspectral information to estimate acidity, moisture and peroxides in olive oil samples

Efficient in‐field plant phenomics for row‐crops with an autonomous ground vehicle

Analytical robustness of quantitative NIR chemical imaging for Islamic paper characterization

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