基于光谱特征和生理特征的番茄磷营养诊断方法  

Tomatoes Phosphorus Nutrition Diagnosis Based on Spectral and Physiological Characteristics

作　　者：李美清[1] 李晋阳[1] 毛罕平[1] 

机构地区：[1]江苏大学现代农业装备与技术教育部重点实验室,镇江212013

出　　处：《农业机械学报》2016年第3期286-291,共6页Transactions of the Chinese Society for Agricultural Machinery

基　　金：'十二五'国家科技支撑计划项目(2014BAD08B03);江苏大学高级人才基金项目(13JDG077);江苏省博士后基金项目(1402076B);江苏高校优势学科建设工程项目(苏政办发[2014]37号)

摘　　要：为提高番茄磷营养水平检测精度,针对目前基于光谱分析的作物磷营养水平检测精度较低以及磷的光谱反射率受叶绿素和花青素影响的问题,提出了结合番茄样本光谱特征和生理特征的番茄磷营养水平诊断策略。以自行培育的25%、50%、75%、100%、150%5个梯度水平的磷营养胁迫水培番茄样本为研究对象,分别利用光谱分析仪和叶绿素仪采集不同磷营养水平番茄叶片的光谱数据和SPAD值,并对叶片花青素含量进行测定,提取各样本在不同波长下的光谱反射率和生理特征(SPAD值和花青素含量)作为番茄磷营养诊断的特征变量,基于最小二乘支持向量机建立诊断模型,通过改进粒子群优化算法获取支持向量机的最优参数。将120个番茄样本随机分为训练集和测试集分别进行实验。结果表明,采用本文的建模方法结合番茄样本光谱特征和生理特征能够建立精度较高的番茄磷营养水平预测模型,高于对比的其他方法,其相关系数和均方根误差分别为0.961 1和0.461,诊断效果较好,为番茄磷素的快速检测提供了新思路。In order to improve detection precision of crop phosphorus（ P） nutrition level,in view of the problem that the present detection precision of crop phosphorus nutrition level based on spectral analysis is low and the spectral reflectance of phosphorus was influenced by both chlorophyll and anthocyanin,a phosphorus nutrition diagnosis strategy was proposed by fusing spectrum characteristics and physiological characteristics of tomato samples. With five levels（ 25%,50%,75%,100% and 150%） of P nutrition stress samples cultivated by soilless cultivation mode as the research objects,reflectance spectra of different nutrient deficiency greenhouse tomato leaves was acquired by spectrum analyzer as well as the SPAD values of tomato leaves were obtained by SPAD-502. In addition,anthocyanin contents in leaves were determined. By using the spectral reflectance data under four characteristic wavelengths and physiological characteristics（ anthocyanin content and SPAD value） as characteristic variables for tomato phosphorus nutrition diagnosis,the P nutrition diagnosis model was built based on least squares support vector machine（ LS-SVM）. An improved particle swarm optimization（ IPSO） —adaptive inertial weight particle swarm optimization（ AIWPSO） was designed to search the optimum values of SVM parameters for improving the search efficiency and avoiding getting lock in the local optimization. The proposed method with reflectance spectral and physiological characteristics（ model 1） was compared with other threedifferent models. For model 2,the method was same as the model 1 with the spectral features data only,model 3 was traditional LS-SVM which the optimum values of SVM parameters were obtained by cross validation of spectral and physiological characteristics data and model 4 was same as the model 3 with the spectral features data only. The results showed that the correlation coefficient and root mean square error of P were 0. 961 1 and 0. 461,respectively,higher than those of other methods prese

关 键 词：番茄 磷 光谱 营养诊断 支持向量回归 

分 类 号：S123[农业科学—农业基础科学] TP79[自动化与计算机技术—检测技术与自动化装置]