Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content

Yang, Jian; Shi, Shuo; Gong, Wei; Du, Lina; Y., Y.; Zhu, Bin; Song, Shalei

doi:10.17221/7/2015-pse

Cited by 27 publications

(18 citation statements)

References 18 publications

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“…Fluorescence ratios (F 740 /F 685 and F 685 /F 525 ) were widely utilized for the estimation of the nitrogen status and for the diagnosis of crop growth because of their simplicity in measurement and interpretation [11,46]. However, studies of Yang Jian et al [47] indicated that these fluorescence ratios were insensitive to the rice LNC. On the contrary, the rice LNC was closely correlated with the intensity of the fluorescence peaks (F 450 , F 685 and F 740 ).…”

Section: Lnc Estimation Using Reflectance Spectrum and Reflectance + mentioning

confidence: 99%

Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data

Shi

Yang

et al. 2016

Remote Sensing

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Abstract:Nitrogen is an essential nutrient element in crop photosynthesis and yield improvement. Thus, it is urgent and important to accurately estimate the leaf nitrogen contents (LNC) of crops for precision nitrogen management. Based on the correlation between LNC and reflectance spectra, the hyperspectral LiDAR (HSL) system can determine three-dimensional structural parameters and biochemical changes of crops. Thereby, HSL technology has been widely used to monitor the LNC of crops at leaf and canopy levels. In addition, the laser-induced fluorescence (LIF) of chlorophyll, related to the histological structure and physiological conditions of green plants, can also be utilized to detect nutrient stress in crops. In this study, four regression algorithms, support vector machines (SVMs), partial least squares (PLS) and two artificial neural networks (ANNs), back propagation NNs (BP-NNs) and radial basic function NNs (RBF-NNs), were selected to estimate rice LNC in booting and heading stages based on reflectance and LIF spectra. These four regression algorithms were used for 36 input variables, including the reflectance spectral variables on 32 wavelengths and four peaks of the LIF spectra. A feature weight algorithm was proposed to select different band combinations for the LNC retrieval models. The determination coefficient (R 2 ) and the root mean square error (RMSE) of the retrieval models were utilized to compare their abilities of estimating the rice LNC. The experimental results demonstrate that (I) these four regression methods are useful for estimating rice LNC in the order of RBF-NNs > SVMs > BP-NNs > PLS; (II) The LIF data in two forms, including peaks and indices, display potential in rice LNC retrieval, especially when using the PLS regression (PLSR) model for the relationship of rice LNC with spectral variables. The feature weighting algorithm is an effective and necessary method to determine appropriate band combinations for rice LNC estimation.

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Section: Lnc Estimation Using Reflectance Spectrum and Reflectance + mentioning

confidence: 99%

Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data

Shi

Yang

et al. 2016

Remote Sensing

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“…Related studies showed that the characteristic peaks of LIF spectra were closely related to the nutrient stress of crops (Tuba et al 2010, Yang et al 2015a). Due to the advantages of rapid, non-destruction and without pre-processing analysis, it has been widely used to estimate canopy N status of maize, tomato, wheat and paddy rice , Yang et al 2015b, Živčák et al 2014a). However, they mainly focused on using the far-red and red fluorescence ratios, fluorescence intensity as well as non-photochemical quenching etc.…”

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confidence: 99%

Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice

Yang¹,

Gong²,

Shi³

et al. 2016

Plant Soil Environ.

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Paddy rice is one of the most important cereal crops in China. Nitrogen (N) is closely related to crops production by influencing the photosynthetic efficiency of paddy rice. In this study, laser-induced fluorescence (LIF) technology with the help of principal component analysis (PCA) and back-propagation neural network (BPNN) is proposed to monitor leaf N content (LNC) of paddy rice. The PCA is utilized to extract the characteristic variables of LIF spectra by analysing the major attributes. The results showed that the first three principal components (PCs) can explain 95.76% and 93.53% of the total variance contained in the fluorescence spectra for tillering stage and shooting stage, respectively. Then, BPNN was utilized to inverse the LNC on the basis of the first three PCs as input variables and can obtain the satisfactory inversion results (R 2 of tillering stage and shooting stage are 0.952 and 0.931, respectively; residual main range from -0.2 to 0.2 mg/g). The experimental results demonstrated that LIF technique combined with multivariate analysis will be a useful method for monitoring the LNC of paddy rice, which can provide consultations for the decision-making of peasants in their N fertilization strategies.

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“…The reason is that its N content was lower than other treatments (Figure 1). In addition, compared with F735/F685, the fluorescence ratio of F735/F460 and F685/ F460 both exhibited significant differences among different N fertilization levels in and 2015. McMurtrey et al (1994 and Subhash and Mohanan (1994) achieved the same variation tendency of fluorescence characteristics for different dose of N fertilization.…”

mentioning

confidence: 87%

“…In subsequent investigations, LIF technology was utilized to monitor the environment's (e.g., light stress, high temperature and drought (Živčák et al 2008) influence on soybean, wheat and rice. In addition, Yang et al (2015) analysed the correlation between fluorescence peaks and leaf N content in paddy rice. Thus, LIF is a helpful method in monitoring of the nutrient stress of plants.…”

mentioning

confidence: 99%

Application of the chlorophyll fluorescence ratio in evaluation of paddy rice nitrogen status

Yang¹,

Du²,

Gong³

et al. 2017

Plant Soil Environ.

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In this research, laser-induced fluorescence (LIF) technique combined with back-propagation neural network (BPNN) was employed to analyse different nitrogen (N) fertilization levels in paddy rice. Leaf fluorescence characteristics (FLCs) were measured by using the LIF system built in our laboratory and exhibited different FLCs with different nitrogen fertilization levels. The correlation between fluorescence intensity ratios (F685/F460, F735/F460 and F735/F685) and the dose of N fertilization was established and analysed. Then, the BPNN algorithm was utilized to validate that the different N fertilization levels can be classified based on the three FLCs. The overall identification accuracies of 2014 and 2015 were 90% and 92.5%, respectively. Experimental results demonstrated that the three FLCs with the help of multivariate analysis can be served as a helpful tool in the evaluation of paddy rice N fertilization levels. Besides, this study can also provide guidance for the selection of LIF Lidar channels in the following research.

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Application of fluorescence spectrum to precisely inverse paddy rice nitrogen content

Cited by 27 publications

References 18 publications

Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data

Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data

Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice

Application of the chlorophyll fluorescence ratio in evaluation of paddy rice nitrogen status

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