VIS-NIR Hyperspectral Prediction of Soil Organic Matter Based on
Stacking Generalization Model
ZHANG Xiu-quan1, LI Zhi-wei1*, ZHENG De-cong1*, SONG Hai-yan1, WANG Guo-liang2
1. College of Agricultural Engineering, Shanxi Agricultural University, Taigu 030801, China
2. Millet Research Institute, Shanxi Agricultural University, Changzhi 046000, China
Abstract：Accurate prediction of soil organic matter content is helpful in evaluating farmland fertility and provide a data for precision agriculture. In order to solve the problems of low accuracy and weak Generalization ability of a single model for rapid estimation of organic matter content in farmland surface soil. The surface soil of typical cinnamon farmland in Shanxi Province was studied,a Stacked Generalization Model (SGM) was proposed based on VIS-NIR hyperspectral data for predicting organic matter content. Firstly, the original hyperspectral data are smoothed by wavelet, and the reciprocal derivative and logarithmic reciprocal derivative transform are performed on the smoothed data. The feature bands are extracted by correlation coefficient and recursive feature elimination method. At the same time, Ensemble learning Random Forest (RF), Gradient Boosting Decision Tree (GBDT) and eXtreme Gradient Boosting are introduced in machine learning (XGBoost), and Adaboost are used to predict organic matter content through 5-fold cross-validation. Based on the prediction results of the primary learner, Stochastic gradient Descent (SGD) is used as a meta-learner to establish the SGM stack generalization model. The limitation of low accuracy and instability of a single model is broken through to realize the rapid and stable detection of organic matter content. The results show a good correlation between the spectral information and organic matter content after the penultimate differential transformation, and the maximum correlation is -0.611. Compared with the single model, the decision coefficient (R2) and relative analysis error (RPD) of the stacked generalization prediction model are 0.819 and 2.256, respectively, which are 0.055 and 0.323 higher than the average decision coefficient (R2) and relative analysis error (RPD) of other algorithms, respectively. The mean absolute error (MAE) and root mean square error (RMSE) are 1.742 and 2.308 g·kg-1, respectively, which are 0.406 and 0.389 g·kg-1lower than those of other algorithms. The optimization effect is obvious. It can be used to estimate organic matter content in farmland soil surfaces effectively. The results can provide a basis and reference for the rapid detection of organic matter content in farmland soil surface by hyperspectral method.
张秀全，李志伟，郑德聪，宋海燕，王国梁. 基于近红外-可见光高光谱的堆叠泛化模型褐土有机质预测[J]. 光谱学与光谱分析, 2023, 43(03): 903-910.
ZHANG Xiu-quan, LI Zhi-wei, ZHENG De-cong, SONG Hai-yan, WANG Guo-liang. VIS-NIR Hyperspectral Prediction of Soil Organic Matter Based on
Stacking Generalization Model. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 903-910.
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