Purpose

In 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/approach

Based 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.

Findings

The 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 implications

The 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/value

The 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.

中文翻译：

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基于高光谱数据的土壤有机质含量灰色模糊预测模型

目的

为了克服光谱估计的不确定性，提高光谱估计的准确性，本文旨在利用灰色理论和模糊理论建立土壤有机质含量的灰色模糊预测模型。

设计/方法/途径

以山东省济南市章丘区和济阳区121个土壤样品数据为基础，首先采用光谱变换方法对土壤光谱数据进行变换，根据最大相关性原则选取光谱估计因子。然后，利用区间灰度数的广义灰度修正建模样本和测试样本的估计因子，提高相关性。最后，利用模糊识别理论建立了土壤有机质的高光谱预测模型，通过调整模糊分类数对模型进行优化，并利用平均相对误差和判定方法评价模型的估计精度。系数。

发现

结果表明，区间灰度数的广义灰度能有效提高土壤有机质含量与估算因子的相关性，显着提高模型和试验样本的准确性，其中决定系数R 2 = 0.9213 ^， 均值相对误差 (MRE) = 20 个测试样本的 6.3630%。研究表明本文提出的灰色模糊预测模型是可行和有效的，为土壤有机质含量的高光谱估算提供了一种新的途径。

实际影响

研究表明，本文提出的灰色模糊预测模型不仅可以有效处理光谱估计中的三类不确定性，而且可以实现对估计因素的修正，有助于提高建模估计的准确性。研究结果丰富了土壤光谱估计的理论和方法，也为利用三种不确定性理论处理预测问题中的三种不确定性提供了新的思路。

原创性/价值

该文成功地实现了土壤有机质含量高光谱估算的灰色模糊预测模型，有效地处理了光谱估算中的随机性、模糊性和灰色不确定性。