基于PLS-BPNN算法的土壤速效磷高光谱回归预测方法

土壤速效磷是影响作物生长发育的重要养分指标。光谱分析技术对速效磷的定量预测具有较好的应用前景,高光谱带宽窄、分辨率高,但存在数据冗余和共线性等问题。本文针对皖北砂姜黑土145个样本开展研究,提出了利用偏最小二乘回归算法(PLS-R)对土壤可见近红外高光谱数据(400~1 000 nm)进行数据降维和特征提取,根据交叉验证和变量投影重要性分别得到潜在变量和特征波长;再分别输入BP神经网络(BPNN)进行训练,得到回归分析模型对速效磷进行定量预测。结果表明:与利用全部波长数据建模的预测结果(校正集和验证集的相对分析误差M_(RPD)分别为10.27和2.09)相比,利用9个特征波长建立的回归模型校正集M_(RPD)为2.66,预测精度明显降低,而验证集M_(RPD)为2.05,近似达到利用全部波长数据建模的预测效果;利用5个潜在变量建立回归模型,校正集和验证集的M_(RPD)分别为3.10和2.29,其中验证集相对于全部波长建模的预测精度提高了9.6%。因此,基于PLS-BPNN算法进行回归建模可以有效降低高光谱数据冗余和共线性的影响,提高模型的泛化能力,且利用潜在变量进行回归建模能提高模型预测精度。

Prediction Method of Soil Available Phosphorus Using Hyperspectral Data Based on PLS-BPNN

Soil available phosphorus (AP) is supposed to be an important nutrient constituent for the growth and development of crops. Hyperspectral analysis has proven to be a rapid and effective means for quantitatively predicting soil AP, which has a good prospect benefit from the narrow bandwidth and the high resolution. However, the existence of multicollinearity and redundant considerably leads to overfitting of the regression model and decrease of the generalization ability. A total of 145 lime concretion black soil samples collected from the Northern Anhui Plain, China, were used as research objects to investigate the prediction performance of the back-propagation neural network (BPNN) based on the partial least square regression (PLS-R) algorithm. The PLS-R was applied to conduct dimensionality reduction and feature selection on the soil visible and near infrared hyperspectral data ranging from 400~1000nm with 339 wavelengths. Five latent variables (LVs) were obtained by the leave one out cross validation, and nine optimal wavelengths were selected by the variable importance in projection (VIP) scores. The BPNN regression models were built with the input of the five latent variables (LVs-BPNN), the nine optimal wavelengths (VIPs-BPNN), and the whole wavelengths (Ws-BPNN), respectively. The ratio of performance to deviation (MRPD) and the ratio of the interpretable sum squared deviation to the real sum squared deviation (MSSR/SST) were selected to evaluate the prediction accuracy and explanatory power of different regression models, respectively. As a result, the prediction accuracies of three BPNN models outperformed the PLS-R model significantly;the VIPs-BPNN model achieved similar performance (MRPD was 2.05, MSSR/SST was 0.79) as the Ws-BPNN model (MRPD was 2.09, MSSR/SST was 0.85) of the validation set, while the MRPD was decreased obviously from 10.27 (Ws-BPNN) to 2.66 (VIPs-BPNN) of the calibration set;the LVs-BPNN model gained the highest prediction accuracy as MRPD was 2.29 of the validation set, even though the MSSR/SST was slightly decreased to 0.76. The results illustrated that the PLS-BPNN models could significantly reduce the degree of overfitting and improve the generalization ability;moreover, the LVs-BPNN model could improve the accuracy of predicting soil AP.