Using Different Data Mining Algorithms to Predict Soil Organic Matter Based on Visible-Near Infrared Spectroscopy
JI Wen-jun1, LI Xi1, LI Cheng-xue2, ZHOU Yin1, SHI Zhou1, 3*
1. Institute of Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China 2. College of Resources and Environment, Yunnan Agricultural University, Kunming 650201, China 3. Cyrus Tang Center for Sensor Materials and Applications, Zhejiang University, Hangzhou 310058, China
Abstract：Using visible/near infrared spectroscopy to model soil properties is very important in current soil sensing research. It can be applied to rapidly access soil information and precision management. In the present study, paddy soil in Zhejiang Province is treated as the research samples. The nonlinear models such as random forests (RF), supported vector machines (SVM) and artificial neural networks (ANN) were used respectively to build models to predict soil organic matter based on different selection of calibration and validation datasets. The results show that there is a certain impact on prediction results under the division of different sample modes. Compared to the commonly used linear model PLSR, the nonlinear model RF and SVM have comparable prediction accuracy, especially predictions by SVM using all Vis-NIR wavelengths produced the smallest RMSE values. It shows that the model constructed by SVM method has a good predictive ability. In addition, a combined method, PLSR-ANN (with the introduction of ANN into PLSR), significantly improves the predictive ability of PLSR. Even though ANNs are “black box” systems the combination of PLSR and nonliner modelling helps achieve good predictions and interpretability.
纪文君1, 李 曦1, 李成学2, 周 银1, 史 舟1, 3* . 基于全谱数据挖掘技术的土壤有机质高光谱预测建模研究[J]. 光谱学与光谱分析, 2012, 32(09): 2393-2398.
JI Wen-jun1, LI Xi1, LI Cheng-xue2, ZHOU Yin1, SHI Zhou1, 3* . Using Different Data Mining Algorithms to Predict Soil Organic Matter Based on Visible-Near Infrared Spectroscopy . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2012, 32(09): 2393-2398.
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