Rapid evaluation of soil fertility in tea plantation based on near-infrared spectroscopy. Issue 9 (21st October 2018)

• Record Type: Journal Article
• Authors: Ning, Jingming; Sheng, Mengge; Yi, Xiaoyun; Wang, Yujie; Hou, Zhiwei; Zhang, Zhengzhu; Gu, Xungang
• Is Part Of: Spectroscopy letters. Volume 51:Issue 9(2018)
• Page Start: 463
• Page End: 471
• Publication Date: 2018-10-21
• Subjects: Fertility discrimination -- quantitative prediction -- soil organic matter -- total nitrogen; Spectrum analysis -- Periodicals; 543.5
• DOI: 10.1080/00387010.2018.1475398
• Languages: English
• ISSNs: 0038-7010

Abstract

Abstract: An efficient soil analysis technique was developed to monitor soil fertility and perform precise soil management in tea plantations. In this study, near-infrared spectroscopy combined with chemometric methods was utilized to determine the organic matter and total nitrogen content and evaluate fertility of tea plantation soils. First, photometric precision and subtractive spectroscopy were used as indicators in identifying optimal sample preparation condition. Spectral reproducibility reached an optimum with powder particle size of 100 mesh (0.149 mm). Second, after comparing the combinations of the partial least squares method with three different characteristic wavelength extraction methods, the genetic algorithm and competitive adaptive reweighted sampling quantitative discrimination models were determined to be optimal for organic matter and total nitrogen contents, with prediction correlation coefficients of 0.9102 and 0.8763, respectively. Third, classification models for soil fertility level using linear discriminant analysis, support vector machine, and extreme learning machine were established based on a full spectrum and successive projections algorithm separately. The successive projections algorithm-extreme learning machine model was deemed superior with a correct classification rate of 84.38%. Our findings demonstrate that the proposed near-infrared spectroscopy calibration models successfully achieve the nondestructive and rapid evaluation of organic matter and total nitrogen contents, as well as the classification of soil fertility levels, in tea plantation soils. The results provide a basis for the development of internet-of-things sensors in the construction of a high-yield and high-quality tea plantation.