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Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat

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Tahmasbian, I., Morgan, N. K., Hosseini Bai, S., Dunlop, M. W. and Moss, A. F. (2021) Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat. Remote Sensing, 13 (6). ISSN 2072-4292

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Article Link(s): https://doi.org/10.3390/rs13061128

Abstract

Hyperspectral imaging (HSI) is an emerging rapid and non-destructive technology that has promising application within feed mills and processing plants in poultry and other intensive animal industries. HSI may be advantageous over near infrared spectroscopy (NIRS) as it scans entire samples, which enables compositional gradients and sample heterogenicity to be visualised and analysed. This study was a preliminary investigation to compare the performance of HSI with that of NIRS for quality measurements of ground samples of Australian wheat and to identify the most important spectral regions for predicting carbon (C) and nitrogen (N) concentrations. In total, 69 samples were scanned using an NIRS (400–2500 nm), and two HSI cameras operated in 400–1000 nm (VNIR) and 1000–2500 nm (SWIR) spectral regions. Partial least square regression (PLSR) models were used to correlate C and N concentrations of 63 calibration samples with their spectral reflectance, with 6 additional samples used for testing the models. The accuracy of the HSI predictions (full spectra) were similar or slightly higher than those of NIRS (NIRS Rc2 for C = 0.90 and N = 0.96 vs. HSI Rc2 for C (VNIR) = 0.97 and N (SWIR) = 0.97). The most important spectral region for C prediction identified using HSI reflectance was 400–550 nm with R2 of 0.93 and RMSE of 0.17% in the calibration set and R2 of 0.86, RMSE of 0.21% and ratio of performance to deviation (RPD) of 2.03 in the test set. The most important spectral regions for predicting N concentrations in the feed samples included 1451–1600 nm, 1901–2050 nm and 2051–2200 nm, providing prediction with R2 ranging from 0.91 to 0.93, RMSE ranging from 0.06% to 0.07% in the calibration sets, R2 from 0.96 to 0.99, RMSE of 0.06% and RPD from 3.47 to 3.92 in the test sets. The prediction accuracy of HSI and NIRS were comparable possibly due to the larger statistical population (larger number of pixels) that HSI provided, despite the fact that HSI had smaller spectral range compared with that of NIRS. In addition, HSI enabled visualising the variability of C and N in the samples. Therefore, HSI is advantageous compared to NIRS as it is a multifunctional tool that poses many potential applications in data collection and quality assurance within feed mills and poultry processing plants. The ability to more accurately measure and visualise the properties of feed ingredients has potential economic benefits and therefore additional investigation and development of HSI in this application is warranted.

Item Type:Article
Business groups:Animal Science
Additional Information:Open access
Keywords:feed ingredients hyperspectral imaging machine learning NIR spectroscopy non-destructive quality monitoring real-time wheat
Subjects:Technology > Technology (General) > Spectroscopy > NIR (Near Infrared)
Agriculture > Agriculture (General) > Soils. Soil science > Soil and crops. Soil-plant relationships. Soil productivity
Agriculture > Agriculture (General) > Fertilisers
Plant culture > Field crops
Plant culture > Field crops > Wheat
Deposited On:18 Mar 2021 04:03
Last Modified:18 Mar 2021 04:03

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