Grey fuzzy prediction model of soil organic matter content using hyper-spectral data

• Published in: Grey systems Vol. 13; no. 2; pp. 357 - 380
• Main Authors: Yu, Jintao, Li, Xican, Cao, Shuang, Liu, Fajun
• Format: Journal Article
• Language: English
• Published: Bingley Emerald Publishing Limited 16.03.2023; Emerald Group Publishing Limited
• Subjects: Accuracy; Content analysis; Correlation; Estimation; Fuzzy sets; Methods; Model accuracy; Model testing; Modelling; Neural networks; Organic matter; Prediction models; Regression analysis; Statistical analysis; Support vector machines; System theory; Uncertainty; Wavelet transforms; Soil organic matter; Estimation factor; Grey fuzzy prediction model; Generalized greyness; Hyper-spectral remote sensing
• ISSN: 2043-9377; 2043-9385
• DOI: 10.1108/GS-08-2022-0089

PurposeIn order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.Design/methodology/approachBased on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.FindingsThe results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R2 = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.Practical implicationsThe research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.Originality/valueThe paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.