CAJ | 학술논문

为建立一种能够同时快速检测土壤全磷和全钾的定量估计模型，该文采用近红外漫反射技术对赣南脐橙果园的土壤进行研究，对56个土样风干、过筛，然后进行光谱采集和化学分析。光谱经过Savitzky-Golay平滑后再用一阶微分变换的方法进行预处理，分别应用偏最小二乘回归（partial least square regress PLS）、主成分回归（principal component regression PCR）和最小二乘支持向量机（least squares support vector machine LS-SVM）3种方法，在4000～7500 cm-1波数范围内，建立赣南脐橙果园土壤全磷和全钾快速定量检测模型。结果发现在建立土壤全磷模型时，PLS 和 PCR 的预测模型效果均不理想，但 LS-SVM 建立的模型较为理想,其预测相关系数（correlation coefficient of prediction RP）为0.884，预测集均方根误差（the root mean square error of prediction RMSEP）为0.341，预测相对分析误差（residual predictive deviation RPD）为2.59。在建立土壤全钾模型时，PLS、PCR和LS-SVM 建立3种模型效果均理想，其中以LS-SVM模型最理想，其预测相关系数（RP）为0.971，预测集均方根误差（RMSEP）为0.714，预测相对分析误差（RPD）为5.12。研究表明，采用LS-SVM建立的土壤全磷和全钾模型对实现土壤全磷和全钾含量快速检测具有可行性。
위건립일충능구동시쾌속검측토양전린화전갑적정량고계모형，해문채용근홍외만반사기술대공남제등과완적토양진행연구，대56개토양풍간、과사，연후진행광보채집화화학분석。광보경과Savitzky-Golay평활후재용일계미분변환적방법진행예처리，분별응용편최소이승회귀（partial least square regress PLS）、주성분회귀（principal component regression PCR）화최소이승지지향량궤（least squares support vector machine LS-SVM）3충방법，재4000～7500 cm-1파수범위내，건립공남제등과완토양전린화전갑쾌속정량검측모형。결과발현재건립토양전린모형시，PLS 화 PCR 적예측모형효과균불이상，단 LS-SVM 건립적모형교위이상,기예측상관계수（correlation coefficient of prediction RP）위0.884，예측집균방근오차（the root mean square error of prediction RMSEP）위0.341，예측상대분석오차（residual predictive deviation RPD）위2.59。재건립토양전갑모형시，PLS、PCR화LS-SVM 건립3충모형효과균이상，기중이LS-SVM모형최이상，기예측상관계수（RP）위0.971，예측집균방근오차（RMSEP）위0.714，예측상대분석오차（RPD）위5.12。연구표명，채용LS-SVM건립적토양전린화전갑모형대실현토양전린화전갑함량쾌속검측구유가행성。
To study the distribution of soil nutrients and build soil models of the total potassium (TK) and the total phosphorus (TP) that could predict the measured value, the soil samples coming from GAN NAN navel orange orchard were collected. The precision of the soil moisture measurement using near-infrared spectra and quantitative analysis model method on the sample condition, soil samples were air-dried and sieved through 0.149 mm screen holes after grinding. The portable spectroradiometer of BRUKER TENSOR 37 with a full spectral wavelength of 400-2500nm, was used to scan the soil samples with diffuse reflectance spectroscopy and the data validity of the original spectra was averaged. Fifty-nine soil samples were selected, and thirty-seven soil samples were used to build the calibration model and nineteen were used to build the prediction model. Two kinds of data pretreatment methods including Savizky-Golay smoothing and the first order derivative were used to pretreat the soil sample spectra. The preprocessed by the combination of first-order derivative and moving average filter were used and the calibration models were developed by the partial least square regress (PLS), principal component regression (PCR) and least squares support vector machine(LS-SVM)based on the spectral data and measured values, which the models could quickly and accurately estimate the soil contents of total potassium (TK) and the total phosphorus (TP) in the wave-number of 4000-7500cm-1. The model accuracy was evaluated using the correlation coefficient of prediction (RP), the root mean square error of prediction (RMSEP), and the residual predictive deviation (RPD). The results show that the least squares support vector machine (LS-SVM) model of total phosphorus (TP) gave the best results with the correlation coefficient(Rp)of 0.884, the root mean square error of prediction (RMSEP) of 0.341, and the residual predictive deviation (RPD) of 2.59. The least squares support vector machine (LS-SVM) model of the TK gave the best result with the correlation coefficient (Rp) of 0.971 and the root mean square error of prediction (RMSEP) of 0.714, the residual predictive deviation (RPD) with the higher being the better, and the high value measured at 5.12. The experiments showed that the diffuse reflectance near infrared can be quickly and accurately estimated to the TK contents and the TP contents in soil samples using the least squares support vector machine method and this study provided a scientific basis for quickly detection of soil TP and TK by near infrared spectroscopy technology.