Measurement of nitrogen content in rice plant using near infrared spectroscopy combined with different PLS algorithms

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Molecular and biomolecular spectroscopy, 2023-01, Vol.284, p.121733, Article 121733
• Main Authors: Miao, XueXue, Miao, Ying, Liu, Yang, Tao, ShuHua, Zheng, HuaBin, Wang, JieMin, Wang, WeiQin, Tang, QiYuan
• Format: Article
• Language: English
• Subjects: Characteristic selection; Near-infrared spectroscopy; Nitrogen content; Oryza sativa; Partial least squares regression
• ISSN: 1386-1425
• DOI: 10.1016/j.saa.2022.121733

[Display omitted] •Nitrogen content is an important index for evaluating the vigour of rice.•The rice plant spectra processed with SNV + FD facilitate nitrogen identication.•Different methods were employed for spectral subintervals selection and model optimization.•SiPLS algorithm successfully extract sensitive wavenumbers and obtain the optimal result.•NIRS combined with SiPLS can applied to monitor the nitrogen nutrient status real-timely during key stages of rice growth. Nitrogen plays an important role in rice growth, and determination of nitrogen content in rice plants is of great significance in assessing plant nutritional status and allowing precision cultivation. Traditional chemical methods for determining nitrogen content have the disadvantages of destructive sampling and lengthy analysis times. Here, the feasibility of rapid nitrogen content analysis by near-infrared (NIR) spectroscopy of rice plants was studied. Spectral data from 447 rice samples at several growth stages were used to establish a predictive model. Different spectral preprocessing methods and characteristic selection methods were compared, such as interval partial least-squares (iPLS), synergy interval partial least-squares (SiPLS), and moving-window partial least-squares (mwPLS). The SiPLS method exhibited better performance than mwPLS or iPLS. Specifically, the combination of four subintervals (7, 26, 27, and 28), with characteristic bands at 5299–4451 cm−1 and 10445–10423 cm−1, resulted in the best model. The optimal SiPLS model had a correlation coefficient of 0.9533 and a root mean square error of prediction (RMSEP) of 0.1952 on the prediction set. Compared to using the full spectra, using SiPLS reduced the number of characteristics by 87 % in the model, and RMSEP was reduced from 0.2284 to 0.1952. The results demonstrate that NIR spectroscopy combined with the SiPLS algorithm can be applied to quickly determine nitrogen content in rice plants. This study provides a technical framework to guide future precision agriculture efforts with respect to nitrogen application.