title: [Estimation of sugar to nitrogen ratio in wheat leaves with near infrared spectrometry].
abstract: The soluble sugar to nitrogen ratio reflects the coordination degree of carbon (C) and nitrogen (N) metabolism. Precise and real-time monitoring of soluble sugar to nitrogen ratio is of significant importance for nitrogen diagnosis and management regulation in wheat production. In this study, time-course near infrared spectroscopy and soluble sugar to nitrogen ratio of fresh and dry leaves were obtained under different field experiments with varied years and cultivar and N rates. The methods of partial least squares (PLS), back-propagation neural network (BPNN) and wavelet neural network (WNN) were used to develop the calibration models with the preprocessed spectra, respectively, and the dataset selected randomly was used to evaluate the constructed models. The results showed that the performance of the models for fresh-leaves was not satisfied, but good for dry-leaves with the root mean square errors of prediction (RMSEP) by PLS, BPNN and WNN models based on 1655-2378 nm less than 0.3% and with the coefficients of determination (R2) over than 0.9, respectively. In comparison, the model based on WNN was the best one. All these indicated that near infrared spectrometry could be applied to estimating the soluble sugar to nitrogen ratio in plant. The results provided the theoretical basis and technological approach for diagnosing crop C/N.
authors.forename: Xia
authors.initials: X
authors.lastname: Yao
authors.forename: Xue
authors.initials: X
authors.lastname: Wang
authors.forename: Yu
authors.initials: Y
authors.lastname: Huang
authors.forename: Shou-peng
authors.initials: SP
authors.lastname: Tang
authors.forename: Yong-chao
authors.initials: YC
authors.lastname: Tian
authors.forename: Wei-xing
authors.initials: WX
authors.lastname: Cao
authors.forename: Yan
authors.initials: Y
authors.lastname: Zhu
mesh_terms: D002138:Calibration; D002241:Carbohydrates / Q000737:chemistry*; D002244:Carbon / Q000737:chemistry*; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009584:Nitrogen / Q000737:chemistry*; D018515:Plant Leaves / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared; D014908:Triticum / Q000737:chemistry*
chemical_list: D002241:Carbohydrates; D002244:Carbon; D009584:Nitrogen