基于高光谱的水稻叶片氮含量估计的深度森林模型研究

李金敏, 陈秀青, 杨琦, 史良胜

Deep learning models for estimation of paddy rice leaf nitrogen concentration based on canopy hyperspectral data

LI Jin-Min, CHEN Xiu-Qing, YANG Qi, SHI Liang-Sheng

表4 随机森林和支持向量机对水稻叶片含氮量估算效果分析

Table 4 Results of model performance analysis by random forest and support vector machine (SVM)

回归模型 Regression model 光谱波段 Band 范围 Range (nm) 建模集 Calibration set 预测集 Prediction set R2c RMSEc R2p RMSEp 随机森林 Random forest 全波段Full-wave band 350-2500 0.982 0.149 0.891 0.378 可见光波段Visible waveband 400-760 0.976 0.171 0.804 0.507 近红外波段Near-infrared waveband 760-2500 0.980 0.156 0.890 0.380 支持向量机 SVM 全波段Full-wave band 350-2500 0.960 0.223 0.725 0.601 可见光波段Visible waveband 400-760 0.843 0.440 0.755 0.568 近红外波段Near-infrared waveband 760-2500 0.936 0.281 0.697 0.631