Estimating Canopy Nitrogen Content of Rice Using Hyperspectral Reflectance Combined with SG-FD-CARS-ELM in Cold Region

Wang Shuwen; Xiu Cheng; Zhao Qinghe; Li Xiaowei; Zhang Yan; A Mani; Wang Runtao

doi:doi:10.11648/j.ijimse.20180304.11

| Peer-Reviewed |

Estimating Canopy Nitrogen Content of Rice Using Hyperspectral Reflectance Combined with SG-FD-CARS-ELM in Cold Region

Wang Shuwen, Xiu Cheng, Zhao Qinghe, Li Xiaowei, Zhang Yan, A Mani, Wang Runtao

Received: 15 November 2018 Accepted: 4 December 2018 Published: 28 December 2018

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Abstract

In this study, visible and near infrared hyperspectral imaging technique was used to predict canopy leaf nitrogen content (CLNC) of rice in cold region. Canopy hyperspectral images of rice were acquired at tillering, jointing and heading stage, respectively. Original spectra was extracted using ENVI5.0 software, and leaf nitrogen content was obtained by chemical analysis method. 5 pre-processing methods of savitzky-golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative (FD) and second derivative (SD) were used to eliminate unexpected noise. After comparing the performance of PLSR models based on spectra of full wavelengths after pre-processing, SG combined with FD had the best performance for eliminating the noise interference and improving the performance of models. In order to further simplify and enhance the models, 3 variable selection methods of successive projections algorithm (SPA), uninformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS) were used to select the characteristic wavelengths, and partial least square regression (PLSR) and extreme learning machine (ELM) were used to establish prediction models. After comparing the performance of PLSR models and ELM models, CARS could effectively select the wavelengths that had strong information and were not sensitive to external disturbance factors, and the nonlinear ELM model was more suitable for predicting CLNC of rice in cold region, the specific values of RC² and RP² of ELM models based on CARS were 0.906 and 0.888 for tillering stage, 0.903 and 0.892 for jointing stage, and 0.894, 0.887 for heading stage, respectively. The results of this study could provide a reference for quantitative analysis of nitrogen content of rice using hyperspectral technology.

DOI	10.11648/j.ijimse.20180304.11
Published in	International Journal of Industrial and Manufacturing Systems Engineering (Volume 3, Issue 4, July 2018)
Page(s)	25-34
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Hyperspectral Imaging, Rice, Canopy Nitrogen Content, Pre-processing, Wavelength Selection

References

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APA Style

Wang Shuwen, Xiu Cheng, Zhao Qinghe, Li Xiaowei, Zhang Yan, et al. (2018). Estimating Canopy Nitrogen Content of Rice Using Hyperspectral Reflectance Combined with SG-FD-CARS-ELM in Cold Region. International Journal of Industrial and Manufacturing Systems Engineering, 3(4), 25-34. https://doi.org/10.11648/j.ijimse.20180304.11

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ACS Style

Wang Shuwen; Xiu Cheng; Zhao Qinghe; Li Xiaowei; Zhang Yan, et al. Estimating Canopy Nitrogen Content of Rice Using Hyperspectral Reflectance Combined with SG-FD-CARS-ELM in Cold Region. Int. J. Ind. Manuf. Syst. Eng. 2018, 3(4), 25-34. doi: 10.11648/j.ijimse.20180304.11

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AMA Style

Wang Shuwen, Xiu Cheng, Zhao Qinghe, Li Xiaowei, Zhang Yan, et al. Estimating Canopy Nitrogen Content of Rice Using Hyperspectral Reflectance Combined with SG-FD-CARS-ELM in Cold Region. Int J Ind Manuf Syst Eng. 2018;3(4):25-34. doi: 10.11648/j.ijimse.20180304.11

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@article{10.11648/j.ijimse.20180304.11,
  author = {Wang Shuwen and Xiu Cheng and Zhao Qinghe and Li Xiaowei and Zhang Yan and A Mani and Wang Runtao},
  title = {Estimating Canopy Nitrogen Content of Rice Using Hyperspectral Reflectance Combined with SG-FD-CARS-ELM in Cold Region},
  journal = {International Journal of Industrial and Manufacturing Systems Engineering},
  volume = {3},
  number = {4},
  pages = {25-34},
  doi = {10.11648/j.ijimse.20180304.11},
  url = {https://doi.org/10.11648/j.ijimse.20180304.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijimse.20180304.11},
  abstract = {In this study, visible and near infrared hyperspectral imaging technique was used to predict canopy leaf nitrogen content (CLNC) of rice in cold region. Canopy hyperspectral images of rice were acquired at tillering, jointing and heading stage, respectively. Original spectra was extracted using ENVI5.0 software, and leaf nitrogen content was obtained by chemical analysis method. 5 pre-processing methods of savitzky-golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative (FD) and second derivative (SD) were used to eliminate unexpected noise. After comparing the performance of PLSR models based on spectra of full wavelengths after pre-processing, SG combined with FD had the best performance for eliminating the noise interference and improving the performance of models. In order to further simplify and enhance the models, 3 variable selection methods of successive projections algorithm (SPA), uninformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS) were used to select the characteristic wavelengths, and partial least square regression (PLSR) and extreme learning machine (ELM) were used to establish prediction models. After comparing the performance of PLSR models and ELM models, CARS could effectively select the wavelengths that had strong information and were not sensitive to external disturbance factors, and the nonlinear ELM model was more suitable for predicting CLNC of rice in cold region, the specific values of RC2 and RP2 of ELM models based on CARS were 0.906 and 0.888 for tillering stage, 0.903 and 0.892 for jointing stage, and 0.894, 0.887 for heading stage, respectively. The results of this study could provide a reference for quantitative analysis of nitrogen content of rice using hyperspectral technology.},
 year = {2018}
}

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TY  - JOUR
T1  - Estimating Canopy Nitrogen Content of Rice Using Hyperspectral Reflectance Combined with SG-FD-CARS-ELM in Cold Region
AU  - Wang Shuwen
AU  - Xiu Cheng
AU  - Zhao Qinghe
AU  - Li Xiaowei
AU  - Zhang Yan
AU  - A Mani
AU  - Wang Runtao
Y1  - 2018/12/28
PY  - 2018
N1  - https://doi.org/10.11648/j.ijimse.20180304.11
DO  - 10.11648/j.ijimse.20180304.11
T2  - International Journal of Industrial and Manufacturing Systems Engineering
JF  - International Journal of Industrial and Manufacturing Systems Engineering
JO  - International Journal of Industrial and Manufacturing Systems Engineering
SP  - 25
EP  - 34
PB  - Science Publishing Group
SN  - 2575-3142
UR  - https://doi.org/10.11648/j.ijimse.20180304.11
AB  - In this study, visible and near infrared hyperspectral imaging technique was used to predict canopy leaf nitrogen content (CLNC) of rice in cold region. Canopy hyperspectral images of rice were acquired at tillering, jointing and heading stage, respectively. Original spectra was extracted using ENVI5.0 software, and leaf nitrogen content was obtained by chemical analysis method. 5 pre-processing methods of savitzky-golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative (FD) and second derivative (SD) were used to eliminate unexpected noise. After comparing the performance of PLSR models based on spectra of full wavelengths after pre-processing, SG combined with FD had the best performance for eliminating the noise interference and improving the performance of models. In order to further simplify and enhance the models, 3 variable selection methods of successive projections algorithm (SPA), uninformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS) were used to select the characteristic wavelengths, and partial least square regression (PLSR) and extreme learning machine (ELM) were used to establish prediction models. After comparing the performance of PLSR models and ELM models, CARS could effectively select the wavelengths that had strong information and were not sensitive to external disturbance factors, and the nonlinear ELM model was more suitable for predicting CLNC of rice in cold region, the specific values of RC2 and RP2 of ELM models based on CARS were 0.906 and 0.888 for tillering stage, 0.903 and 0.892 for jointing stage, and 0.894, 0.887 for heading stage, respectively. The results of this study could provide a reference for quantitative analysis of nitrogen content of rice using hyperspectral technology.
VL  - 3
IS  - 4
ER  -

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Author Information

Wang Shuwen

College of Information Engineering, Lingnan Normal University, Zhanjiang, China; College of Electrical and Information Engineering, Northeast Agricultural University, Harbin, China
Xiu Cheng

College of Electrical and Information Engineering, Northeast Agricultural University, Harbin, China
Zhao Qinghe

College of Electrical and Information Engineering, Northeast Agricultural University, Harbin, China
Li Xiaowei

College of Electrical and Information Engineering, Northeast Agricultural University, Harbin, China
Zhang Yan

College of Electrical and Information Engineering, Northeast Agricultural University, Harbin, China
A Mani

College of Electrical and Information Engineering, Northeast Agricultural University, Harbin, China
Wang Runtao

College of Information Engineering, Lingnan Normal University, Zhanjiang, China

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Plain Text BibTeX RIS

APA Style

Wang Shuwen, Xiu Cheng, Zhao Qinghe, Li Xiaowei, Zhang Yan, et al. (2018). Estimating Canopy Nitrogen Content of Rice Using Hyperspectral Reflectance Combined with SG-FD-CARS-ELM in Cold Region. International Journal of Industrial and Manufacturing Systems Engineering, 3(4), 25-34. https://doi.org/10.11648/j.ijimse.20180304.11

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ACS Style

Wang Shuwen; Xiu Cheng; Zhao Qinghe; Li Xiaowei; Zhang Yan, et al. Estimating Canopy Nitrogen Content of Rice Using Hyperspectral Reflectance Combined with SG-FD-CARS-ELM in Cold Region. Int. J. Ind. Manuf. Syst. Eng. 2018, 3(4), 25-34. doi: 10.11648/j.ijimse.20180304.11

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AMA Style

Wang Shuwen, Xiu Cheng, Zhao Qinghe, Li Xiaowei, Zhang Yan, et al. Estimating Canopy Nitrogen Content of Rice Using Hyperspectral Reflectance Combined with SG-FD-CARS-ELM in Cold Region. Int J Ind Manuf Syst Eng. 2018;3(4):25-34. doi: 10.11648/j.ijimse.20180304.11

Copy | Download

@article{10.11648/j.ijimse.20180304.11,
  author = {Wang Shuwen and Xiu Cheng and Zhao Qinghe and Li Xiaowei and Zhang Yan and A Mani and Wang Runtao},
  title = {Estimating Canopy Nitrogen Content of Rice Using Hyperspectral Reflectance Combined with SG-FD-CARS-ELM in Cold Region},
  journal = {International Journal of Industrial and Manufacturing Systems Engineering},
  volume = {3},
  number = {4},
  pages = {25-34},
  doi = {10.11648/j.ijimse.20180304.11},
  url = {https://doi.org/10.11648/j.ijimse.20180304.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijimse.20180304.11},
  abstract = {In this study, visible and near infrared hyperspectral imaging technique was used to predict canopy leaf nitrogen content (CLNC) of rice in cold region. Canopy hyperspectral images of rice were acquired at tillering, jointing and heading stage, respectively. Original spectra was extracted using ENVI5.0 software, and leaf nitrogen content was obtained by chemical analysis method. 5 pre-processing methods of savitzky-golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative (FD) and second derivative (SD) were used to eliminate unexpected noise. After comparing the performance of PLSR models based on spectra of full wavelengths after pre-processing, SG combined with FD had the best performance for eliminating the noise interference and improving the performance of models. In order to further simplify and enhance the models, 3 variable selection methods of successive projections algorithm (SPA), uninformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS) were used to select the characteristic wavelengths, and partial least square regression (PLSR) and extreme learning machine (ELM) were used to establish prediction models. After comparing the performance of PLSR models and ELM models, CARS could effectively select the wavelengths that had strong information and were not sensitive to external disturbance factors, and the nonlinear ELM model was more suitable for predicting CLNC of rice in cold region, the specific values of RC2 and RP2 of ELM models based on CARS were 0.906 and 0.888 for tillering stage, 0.903 and 0.892 for jointing stage, and 0.894, 0.887 for heading stage, respectively. The results of this study could provide a reference for quantitative analysis of nitrogen content of rice using hyperspectral technology.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Estimating Canopy Nitrogen Content of Rice Using Hyperspectral Reflectance Combined with SG-FD-CARS-ELM in Cold Region
AU  - Wang Shuwen
AU  - Xiu Cheng
AU  - Zhao Qinghe
AU  - Li Xiaowei
AU  - Zhang Yan
AU  - A Mani
AU  - Wang Runtao
Y1  - 2018/12/28
PY  - 2018
N1  - https://doi.org/10.11648/j.ijimse.20180304.11
DO  - 10.11648/j.ijimse.20180304.11
T2  - International Journal of Industrial and Manufacturing Systems Engineering
JF  - International Journal of Industrial and Manufacturing Systems Engineering
JO  - International Journal of Industrial and Manufacturing Systems Engineering
SP  - 25
EP  - 34
PB  - Science Publishing Group
SN  - 2575-3142
UR  - https://doi.org/10.11648/j.ijimse.20180304.11
AB  - In this study, visible and near infrared hyperspectral imaging technique was used to predict canopy leaf nitrogen content (CLNC) of rice in cold region. Canopy hyperspectral images of rice were acquired at tillering, jointing and heading stage, respectively. Original spectra was extracted using ENVI5.0 software, and leaf nitrogen content was obtained by chemical analysis method. 5 pre-processing methods of savitzky-golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative (FD) and second derivative (SD) were used to eliminate unexpected noise. After comparing the performance of PLSR models based on spectra of full wavelengths after pre-processing, SG combined with FD had the best performance for eliminating the noise interference and improving the performance of models. In order to further simplify and enhance the models, 3 variable selection methods of successive projections algorithm (SPA), uninformative variable elimination (UVE) and competitive adaptive reweighted sampling (CARS) were used to select the characteristic wavelengths, and partial least square regression (PLSR) and extreme learning machine (ELM) were used to establish prediction models. After comparing the performance of PLSR models and ELM models, CARS could effectively select the wavelengths that had strong information and were not sensitive to external disturbance factors, and the nonlinear ELM model was more suitable for predicting CLNC of rice in cold region, the specific values of RC2 and RP2 of ELM models based on CARS were 0.906 and 0.888 for tillering stage, 0.903 and 0.892 for jointing stage, and 0.894, 0.887 for heading stage, respectively. The results of this study could provide a reference for quantitative analysis of nitrogen content of rice using hyperspectral technology.
VL  - 3
IS  - 4
ER  -

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