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

作　　者：岳学军[1,2,3] 全东平[1,2,3] 洪添胜[1,2,3] 王健[1,2,3] 瞿祥明[1,2,3] 甘海明[1,2,3] 

机构地区：[1]华南农业大学南方农业机械与装备关键技术教育部重点实验室,广州510642 [2]国家柑橘产业技术体系机械研究室,广州510642 [3]华南农业大学工程学院,广州510642

出　　处：《农业工程学报》2015年第1期294-302,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金(30871450);广东省自然科学基金项目(S2012010009856);广州市科技计划项目(7414558112697)资助

摘　　要：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

关 键 词：叶绿素 主成分分析 无损检测 高光谱 柑橘叶片 支持向量机回归 偏最小二乘回归 

分 类 号：S24[农业科学—农业电气化与自动化]