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Vis/NIR reflectance spectroscopy for hybrid rice variety identification and chlorophyll content evaluation for different nitrogen fertilizer levels.

ABSTRACT: Nitrogen is one of the most important nutrient indicators for the growth of crops, and is closely related to the chlorophyll content of leaves and thus influences the photosynthetic ability of the crops. In this study, five hybrid rice varieties were cultivated during one entire growing period in one experimental field supplied with six nitrogen fertilizer levels. Visible and near infrared (vis/NIR) reflectance spectroscopy combined with multivariate analysis was used to identify hybrid rice varieties and nitrogen fertilizer levels, as well as to detect chlorophyll content associated with nitrogen levels. The support vector machine (SVM) algorithm was applied to identify five varieties of hybrid rice and six levels of nitrogen fertilizer. The results demonstrated that different varieties of hybrid rice for each nitrogen level can be well distinguished except for the highest nitrogen level, and no nitrogen level for each rice variety can be completely identified from the other five nitrogen levels. Further, 12 spectral indices combined with partial least square (PLS) analysis were applied for estimating chlorophyll content of rice leaves from plants subjected to different nitrogen levels, and a root mean square error of cross-validation (RMSECV) of 0.506, a coefficient of determination (R 2) of 97.8% and a ratio of performance to deviation (RPD) of 4.6 for all rice varieties indicated this as a preferable procedure. This study demonstrates that Vis/NIR spectroscopy can have a great potential for identification of rice varieties and evaluation of nitrogen fertilizer levels.

SUBMITTER: Zhang H

PROVIDER: S-EPMC6837182 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Vis/NIR reflectance spectroscopy for hybrid rice variety identification and chlorophyll content evaluation for different nitrogen fertilizer levels.

Zhang Hao H Duan Zheng Z Li Yiyun Y Zhao Guangyu G Zhu Shiming S Fu Wei W Peng Ting T Zhao Quanzhi Q Svanberg Sune S Hu Jiandong J

Royal Society open science 20191023 10

Nitrogen is one of the most important nutrient indicators for the growth of crops, and is closely related to the chlorophyll content of leaves and thus influences the photosynthetic ability of the crops. In this study, five hybrid rice varieties were cultivated during one entire growing period in one experimental field supplied with six nitrogen fertilizer levels. Visible and near infrared (vis/NIR) reflectance spectroscopy combined with multivariate analysis was used to identify hybrid rice var ...[more]

PMID: 31824720

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Project description:Assessment and monitoring of soil organic matter (SOM) quality are important for understanding SOM dynamics and developing management practices that will enhance and maintain the productivity of agricultural soils. Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy (350-2500 nm) has received increasing attention over the recent decades as a promising technique for SOM analysis. While heterogeneity of sample sets is one critical factor that complicates the prediction of soil properties from Vis-NIR spectra, a spectral library representing the local soil diversity needs to be constructed. The study area, covering a surface of 927 km2 and located in Yujiang County of Jiangsu Province, is characterized by a hilly area with different soil parent materials (e.g., red sandstone, shale, Quaternary red clay, and river alluvium). In total, 232 topsoil (0-20 cm) samples were collected for SOM analysis and scanned with a Vis-NIR spectrometer in the laboratory. Reflectance data were related to surface SOM content by means of a partial least square regression (PLSR) method and several data pre-processing techniques, such as first and second derivatives with a smoothing filter. The performance of the PLSR model was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to parent materials). The results showed that the models based on the global calibrations can only make approximate predictions for SOM content (RMSE (root mean squared error) = 4.23-4.69 g kg-1; R2 (coefficient of determination) = 0.80-0.84; RPD (ratio of standard deviation to RMSE) = 2.19-2.44; RPIQ (ratio of performance to inter-quartile distance) = 2.88-3.08). Under the local calibrations, the individual PLSR models for each parent material improved SOM predictions (RMSE = 2.55-3.49 g kg-1; R2 = 0.87-0.93; RPD = 2.67-3.12; RPIQ = 3.15-4.02). Among the four different parent materials, the largest R2 and the smallest RMSE were observed for the shale soils, which had the lowest coefficient of variation (CV) values for clay (18.95%), free iron oxides (15.93%), and pH (1.04%). This demonstrates the importance of a practical subsetting strategy for the continued improvement of SOM prediction with Vis-NIR spectroscopy.

Project description:Coal mining has led to increasingly serious land subsidence, and the reclamation of the subsided land has become a hot topic of concern for governments and scholars. Soil quality of reclaimed land is the key indicator to the evaluation of the reclamation effect; hence, rapid monitoring and evaluation of reclaimed land is of great significance. Visible-near infrared (Vis-NIR) spectroscopy has been shown to be a rapid, timely and efficient tool for the prediction of soil organic carbon (SOC). In this study, 104 soil samples were collected from the Baodian mining area of Shandong province. Vis-NIR reflectance spectra and soil organic carbon content were then measured under laboratory conditions. The spectral data were first denoised using the Savitzky-Golay (SG) convolution smoothing method or the multiple scattering correction (MSC) method, after which the spectral reflectance (R) was subjected to reciprocal, reciprocal logarithm and differential transformations to improve spectral sensitivity. Finally, regression models for estimating the SOC content by the spectral data were constructed using partial least squares regression (PLSR). The results showed that: (1) The SOC content in the mining area was generally low (at the below-average level) and exhibited great variability. (2) The spectral reflectance increased with the decrease of soil organic carbon content. In addition, the sensitivity of the spectrum to the change in SOC content, especially that in the near-infrared band of the original reflectance, decreased when the SOC content was low. (3) The modeling results performed best when the spectral reflectance was preprocessed by Savitzky-Golay (SG) smoothing coupled with multiple scattering correction (MSC) and first-order differential transformation (modeling R2 = 0.86, RMSE = 2.00 g/kg, verification R2 = 0.78, RMSE = 1.81 g/kg, and RPD = 2.69). In addition, the first-order differential of R combined with SG, MSC with R, SG together with MSC and R also produced better modeling results than other pretreatment combinations. Vis-NIR modeling with specific spectral preprocessing methods could predict SOC content effectively.

Dataset Information

Vis/NIR reflectance spectroscopy for hybrid rice variety identification and chlorophyll content evaluation for different nitrogen fertilizer levels.

Publications

Vis/NIR reflectance spectroscopy for hybrid rice variety identification and chlorophyll content evaluation for different nitrogen fertilizer levels.

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