近红外光谱分析在番茄植株生长监测中的应用  

Application of Near Infrared Spectroscopy Analysis in Monitoring Plant Growth of Tomato

作　　者：汪凤珠[1] 李树君[1] 方宪法[1] 毛文华[1] 张小超[1] 

机构地区：[1]中国农业机械化科学研究院土壤植物机器系统技术国家重点实验室,北京100083

出　　处：《农机化研究》2014年第9期197-201,共5页Journal of Agricultural Mechanization Research

基　　金：国家重点基础研究发展计划项目(2012CB723704)

摘　　要：为了克服传统化学检测方法的缺陷,研究了不同氮营养下番茄叶片的近红外光谱特征,利用偏最小二乘回归算法建立了光谱数据与植物生化组分信息的定量分析模型,并采用决定系数R2、交叉校验定标标准差RMSECV、相对分析误差RPD和预测标准差RMSEP验证了模型的优劣。实验表明,所建立的水分、全氮NIR模型的R2分别达到92.35%、86.15%,RMSECV分别为0.346、0.129,RPD分别为3.62、2.69,RMSEP分别为0.209、0.111,预测精度满足实际的测定需求,说明运用近红外漫反射光谱监测番茄植株的生长状况是可行的。In order to overcome the defects of traditional chemical determination method,the spectral characteristics in near- infrared region of tomato samples under different nitrogen nutrition were studied. Partial least- squares regression algorithm was applied to establish models for quantitative analysis between plant biochemical composition information and near- infrared spectral data,while determination coefficient（ R2）,root mean square error of cross validation（ RMSECV）,relative percent deviation（ RPD） and root mean square error of prediction（ RMSEP） were used as four main parameters to evaluate the performance of calibration models. Experiments showed that R2of NIR prediction models built for moisture and total nitrogen reached 92. 35% and 86. 15% respectively,RMSECV were 0. 346 and 0. 129,RPD were 3. 62 and 2. 69,RMSEP were 0. 209 and 0. 111,meeting the practical requirement for content determination. That is, near infrared spectroscopy analysis is a feasible way to monitor growth of tomato.

关 键 词：近红外光谱 番茄 生长监测 偏最小二乘法 定量分析 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置] S123[自动化与计算机技术—控制科学与工程]