柑橘叶片叶绿素含量高光谱无损检测模型

Non-destructive hyperspectral measurement model of chlorophyll content for citrus leaves

针对柑橘叶片叶绿素含量的传统化学检测,不仅耗时长且损伤柑橘叶片,还依赖检测者实操技术,无法集成于精细农业中变量喷施农机具的诸多弊端,该文探讨快速无损检测柑橘叶片叶绿素含量方法。以117棵园栽萝岗甜橙树为研究对象,选用ASD Field Spec 3光谱仪对萌芽期、稳果期、壮果促梢期、采果期共4个生长时期的柑橘叶片进行高光谱反射率采集,并同步采用分光光度法测得叶片的叶绿素含量;以原始光谱及其变换形式作为模型输入矢量,分别在主成分分析(principle component analysis,PCA)降维的基础上利用支持向量机回归(support vector regression,SVR)算法和在小波去噪的基础上利用偏最小二乘回归(partial least square regression,PLSR)算法对柑橘叶片叶绿素含量进行建模预测,全生长期整体建模的校正集和验证集最佳模型决定系数R2分别为0.8713和0.8670,均方根误差RMSE(root-mean-square error)分别为0.1517和0.1544,试验结果表明,高光谱可快速无损地对柑橘叶片叶绿素含量进行精确的定量检测,为柑橘不同生长期的营养监测提供理论依据。

Traditional methods of obtaining chlorophyll content of citrus leaves require grinding citrus leaves and applying chemical titrations, which would be harmful to citrus trees and time-consuming. Besides, it＇s difficult to integrate those chemical methods into variable spraying system as a feedback subsystem. In this paper, we discuss several rapid and non-destructive methods in obtaining chlorophyll content of citrus leaves by using hyperspectral analysis system. Hyperspectral technology obtains synchronously spectrum in continuous space, where we can derive crop growth information visually in a non-destructive way. In this paper, the modeling of chlorophyll content of citrus leaves based on the hyperspectrum was discussed. Field experiments were conducted on 117 planted Luogang citrus trees in the Crab Village of Luogang District, Guangzhou City, Guangdong Province. Hyperspectral reflectance and chlorophyll content of citrus leaves were measured by spectrometer （ASD FieldSpec 3） and traditional spectrophotometry, respectively, during four different growth periods corresponding to germination period, stability period, bloom period and harvesting period. In this way, each sample was presented as an instance-labeled pair, where a high-dimensional vector was regarded as the descriptor along with the measured value of chlorophyll content. All the collected samples constituted a large-scale dataset with totally 468 tuples, 80% of which were utilized as the training set and remaining 20% as the testing set. The model constructed relied on the training set and the testing set was evaluated respectively. Using original spectrum and its transformations as input vector, two models, support vector regression （SVR） based on principle component analysis （PCA） and partial least square regression （PLSR） based on the wavelet denoising were adopted, where PCA was adopted for dimension reduction and the wavelet denoising technique removed high-frequency noise. The two models （SVR and PLSR） were then applied to the final regression analysis for predicting chlorophyll content. The best coefficient of determination （R2） of the calibration set and a validation set of the entire growth period were up to 0.8713 and 0.8670, the root-mean-square error （RMSE） was 0.1517 and 0.1544 respectively. Some main conclusions were obtained：first, when the original reflectance spectrum was used as the input vector and the energy ratio remained 96% for PCA in germination period and stability period, 99% for PCA in bloom period, harvesting period and the whole growth period, SVR with the radial basis function （RBF） as the kernel function achieved the best performance. Second, the wavelet denoising for hyperspectrum data could improve the model performance to some extent. When“sym8”was used as the wavelet basis function,“rigrsure”as the threshold selection,“sln”for rescaling using a single estimation of level noise based on first-level coefficients as the threshold rescaling project and the decomposition layer was 5, PLSR achieved the best result in this research and the coefficient of determination of calibration set and the validation set of the whole growth period were up to 0.8706 and 0.8531, which increased by 8.3%and 9.3%compared with the model without the wavelet denoising. Third, comparative tests between our best model and other models demonstrate the validity and robustness of the two models we derived. Further experimental results revealed that these two models were superior to principle component regression （PCR）, stepwise multiple linear regression （SMLR） and back propagation （BP） neural networks. Finally, hyperspectral technology could obtain accurate chlorophyll content of citrus leaves rapidly, quantitatively and non-destructively, our research may provide a theoretical basis for nutrition surveillance of citrus growth.