CAJ | 학술논문

针对传统柑橘叶片磷含量检测耗时费力、操作繁琐且损伤叶片等弊端，该研究引入高光谱信息探索柑橘叶片磷含量快速无损检测与预测模型，选ASD FieldSpec 3光谱仪采集柑橘4个重要生长期的叶片反射光谱，同步采用硫酸-双氧水消煮-钼锑抗比色法测定叶片的磷含量；先用正交试验确定小波去噪的最佳去噪参数组合，再分别选拉普拉斯特征映射（laplacian eigenmaps，LE）、局部线性嵌入（locally-linear embedding，LLE）、局部切空间对齐（local tangent space alignment， LTSA）、等距映射（isometric mapping，Isomap）和最大方差展开（maximum variance unfolding，MVU）5种典型的流形学习算法对去噪后的光谱数据进行降维和特征提取，进而建立基于支持向量机回归（support vector regression，SVR）的柑橘叶片磷含量预测模型。结果表明，基于一阶导数谱的Isomap-SVR建模结果最佳，全生长期校正集和验证集模型决定系数分别为0.9430和0.8949。试验表明，5种流形学习算法皆适用于对柑橘叶片磷含量的预测，为高光谱检测技术用于柑橘树长势监测和营养诊断提供了参考。
침대전통감귤협편린함량검측모시비력、조작번쇄차손상협편등폐단，해연구인입고광보신식탐색감귤협편린함량쾌속무손검측여예측모형，선ASD FieldSpec 3광보의채집감귤4개중요생장기적협편반사광보，동보채용류산-쌍양수소자-목제항비색법측정협편적린함량；선용정교시험학정소파거조적최가거조삼수조합，재분별선랍보랍사특정영사（laplacian eigenmaps，LE）、국부선성감입（locally-linear embedding，LLE）、국부절공간대제（local tangent space alignment， LTSA）、등거영사（isometric mapping，Isomap）화최대방차전개（maximum variance unfolding，MVU）5충전형적류형학습산법대거조후적광보수거진행강유화특정제취，진이건립기우지지향량궤회귀（support vector regression，SVR）적감귤협편린함량예측모형。결과표명，기우일계도수보적Isomap-SVR건모결과최가，전생장기교정집화험증집모형결정계수분별위0.9430화0.8949。시험표명，5충류형학습산법개괄용우대감귤협편린함량적예측，위고광보검측기술용우감귤수장세감측화영양진단제공료삼고。
Traditional methods of obtaining phosphorus content of citrus leaves are time-consuming procedures with complex operations which can be harmful to citrus trees. More over, traditional methods can not meet the demand of rapid and non-destructive monitoring of phosphorus content in large-scale citrus orchards. In this paper, we presented several models suitable for phosphorus content prediction in 4 growth periods using hyperspectral information. The experiments were conducted in the Crab Village of Luogang District, Guangzhou City, Guangdong Province, and the samples were 195 citrus trees planted. During 4 growth periods, i.e. germination, stability, bloom and picking period, hyperspectral reflectance of citrus leaves was respectively measured by spectrometer (ASD FieldSpec 3), and at the same time, phosphorus content of citrus leaves was obtained by using traditional chemical method. Owing to the high dimensionality and redundancy of raw data, an enhanced algorithm was provided based on manifold learning to deal with the high-dimensional spectral vectors for dimension reduction and feature extraction. First of all, the parameters of wavelet de-noising, which was applied to reduce the high-frequency noise, was determined through orthogonal test, and then 5 manifold learning algorithms, i.e. laplacian eigenmaps (LE), locally-linear embedding (LLE), local tangent space alignment (LTSA), isometric mapping (Isomap) and maximum variance unfolding (MVU) were applied to reduce dimension and extract features for de-noising spectrum. The 5 corresponding prediction models of support vector regression (SVR) for phosphorus content of citrus leaves were established based on their features. Besides, we compared the modeling results of different spectral forms. Some critical conclusions were obtained. First, the optimized parameter combination of wavelet de-noising through orthogonal test was: “coif2” as wavelet basis function, the number of decomposition layer being 7 and “heursure” as the threshold, respectively. Second, the experimental results revealed that these 5 manifold learning algorithms were effective for phosphorus content estimation of citrus leaves. When the raw spectrum was used as the input vector, the Isomap-SVR model achieved better performance than other models;the coefficients of determination for the calibration set were 0.9383, 0.9614, 0.9611, 0.9516 and 0.9430, and the corresponding values of root mean square error (RMSE) were 0.0548, 0.0503, 0.0456, 0.0534 and 0.527 at germination, stability, bloom, picking period and whole growth period, respectively;for the validation set, the coefficients of determination were 0.8866, 0.8923, 0.9236, 0.9005 and 0.8870, and the values of RMSE were 0.0710, 0.0688, 0.0583, 0.0667 and 0.0704, respectively, which meant high usability for the industry. Third, when first derivative spectrum was used as the input vector of the samples with wavelet de-noising, in our research, the Isomap-SVR model achieved the best result and the coefficients of determination for calibration set were 0.9383, 0.9614, 0.9611, 0.9516 and 0.9430 respectively, and the corresponding values of RMSE were 0.0518, 0.0405, 0.0408, 0.0458 and 0.0499 respectively at germination, stability, bloom, picking period and whole growth period;and for the validation set, the coefficients of determination were 0.8913, 0.9107, 0.9373, 0.9135 and 0.8949, and the corresponding values of RMSE were 0.0703, 0.0645, 0.0522, 0.0634 and 0.0659 respectively. Finally, our research proves the feasibility of monitoring phosphorus content of citrus leaves, and may provide a theoretical basis for growth monitoring and nutritional diagnosis of citrus trees.