A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential
To achieve rapid, accurate, and non-destructive diagnoses of nitrogen deficiency in cold land japonica rice, hyperspectral data were collected from field experiments to investigate the relationship between the nitrogen (N) content and the difference in the spectral reflectance relationship and to es...
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doaj-ff3367ec6cda42a798f7cf0eddbd39bb2020-12-08T08:37:04ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2020-12-011110.3389/fpls.2020.573272573272A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance DifferentialFenghua Yu0Fenghua Yu1Shuai Feng2Wen Du3Wen Du4Dingkang Wang5Zhonghui Guo6Simin Xing7Zhongyu Jin8Yingli Cao9Yingli Cao10Tongyu Xu11Tongyu Xu12College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, ChinaLiaoning Engineering Research Center for Information Technology in Agriculture, Shenyang, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, ChinaLiaoning Engineering Research Center for Information Technology in Agriculture, Shenyang, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, ChinaLiaoning Engineering Research Center for Information Technology in Agriculture, Shenyang, ChinaCollege of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, ChinaLiaoning Engineering Research Center for Information Technology in Agriculture, Shenyang, ChinaTo achieve rapid, accurate, and non-destructive diagnoses of nitrogen deficiency in cold land japonica rice, hyperspectral data were collected from field experiments to investigate the relationship between the nitrogen (N) content and the difference in the spectral reflectance relationship and to establish the hyperspectral reflectance difference inversion model of differences in the N content of rice. In this study, the hyperspectral reflectance difference was used to invert the nitrogen deficiency of rice and provide a method for the implementation of precision fertilization without reducing the yield of chemical fertilizer. For the purpose of constructing the standard N content and standard spectral reflectance the principle of minimum fertilizer application at maximum yield was used as a reference standard, and the acquired rice leaf nitrogen content and leaf spectral reflectance were differenced from the standard N content and standard spectral reflectance to obtain N content. The difference and spectral reflectance differential were then subjected to discrete wavelet multiscale decomposition, successive projections algorithm, principal component analysis, and iteratively retaining informative variables (IRIVs); the results were treated as partial least squares (PLSR), extreme learning machine (ELM), and genetic algorithm-extreme learning machine (GA-ELM). The results of hyperspectral dimensionality reduction were used as input to establish the inverse model of N content differential in japonica rice. The results showed that the GA-ELM inversion model established by discrete wavelet multi-scale decomposition obtained the optimal results in data set modeling and training. Both the R2 of the training data set and the validation data set were above 0.68, and the root mean square errors (RMSEs) were <0.6 mg/g and were more predictive, stable, and generalizable than the PLSR and ELM predictive models.https://www.frontiersin.org/articles/10.3389/fpls.2020.573272/fullricehyperspectral reflectance differencenitrogen deficiencydata downscalingELM |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Fenghua Yu Fenghua Yu Shuai Feng Wen Du Wen Du Dingkang Wang Zhonghui Guo Simin Xing Zhongyu Jin Yingli Cao Yingli Cao Tongyu Xu Tongyu Xu |
spellingShingle |
Fenghua Yu Fenghua Yu Shuai Feng Wen Du Wen Du Dingkang Wang Zhonghui Guo Simin Xing Zhongyu Jin Yingli Cao Yingli Cao Tongyu Xu Tongyu Xu A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential Frontiers in Plant Science rice hyperspectral reflectance difference nitrogen deficiency data downscaling ELM |
author_facet |
Fenghua Yu Fenghua Yu Shuai Feng Wen Du Wen Du Dingkang Wang Zhonghui Guo Simin Xing Zhongyu Jin Yingli Cao Yingli Cao Tongyu Xu Tongyu Xu |
author_sort |
Fenghua Yu |
title |
A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential |
title_short |
A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential |
title_full |
A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential |
title_fullStr |
A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential |
title_full_unstemmed |
A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential |
title_sort |
study of nitrogen deficiency inversion in rice leaves based on the hyperspectral reflectance differential |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Plant Science |
issn |
1664-462X |
publishDate |
2020-12-01 |
description |
To achieve rapid, accurate, and non-destructive diagnoses of nitrogen deficiency in cold land japonica rice, hyperspectral data were collected from field experiments to investigate the relationship between the nitrogen (N) content and the difference in the spectral reflectance relationship and to establish the hyperspectral reflectance difference inversion model of differences in the N content of rice. In this study, the hyperspectral reflectance difference was used to invert the nitrogen deficiency of rice and provide a method for the implementation of precision fertilization without reducing the yield of chemical fertilizer. For the purpose of constructing the standard N content and standard spectral reflectance the principle of minimum fertilizer application at maximum yield was used as a reference standard, and the acquired rice leaf nitrogen content and leaf spectral reflectance were differenced from the standard N content and standard spectral reflectance to obtain N content. The difference and spectral reflectance differential were then subjected to discrete wavelet multiscale decomposition, successive projections algorithm, principal component analysis, and iteratively retaining informative variables (IRIVs); the results were treated as partial least squares (PLSR), extreme learning machine (ELM), and genetic algorithm-extreme learning machine (GA-ELM). The results of hyperspectral dimensionality reduction were used as input to establish the inverse model of N content differential in japonica rice. The results showed that the GA-ELM inversion model established by discrete wavelet multi-scale decomposition obtained the optimal results in data set modeling and training. Both the R2 of the training data set and the validation data set were above 0.68, and the root mean square errors (RMSEs) were <0.6 mg/g and were more predictive, stable, and generalizable than the PLSR and ELM predictive models. |
topic |
rice hyperspectral reflectance difference nitrogen deficiency data downscaling ELM |
url |
https://www.frontiersin.org/articles/10.3389/fpls.2020.573272/full |
work_keys_str_mv |
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