李美清,李晋阳,毛罕平.基于光谱特征和生理特征的番茄磷营养诊断方法[J].农业机械学报,2016,47(3):286-291.
Li Meiqing,Li Jinyang,Mao Hanping.Tomatoes Phosphorus Nutrition Diagnosis Based on Spectral and Physiological Characteristics[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(3):286-291.
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基于光谱特征和生理特征的番茄磷营养诊断方法   [下载全文]
Tomatoes Phosphorus Nutrition Diagnosis Based on Spectral and Physiological Characteristics   [Download Pdf][in English]
投稿时间:2015-09-08  
DOI:10.6041/j.issn.1000-1298.2016.03.040
中文关键词:  番茄    光谱  营养诊断  支持向量回归
基金项目:“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)和江苏高校优势学科建设工程项目(苏政办发[2014]37号)
作者单位
李美清 江苏大学 
李晋阳 江苏大学 
毛罕平 江苏大学 
中文摘要: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。
Li Meiqing  Li Jinyang  Mao Hanping
Jiangsu University,Jiangsu University and Jiangsu University
Key Words:tomato  phosphorus  spectrum  nutrition diagnosis  support vector regression
Abstract: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 three different 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 presented in the experiments. It can be concluded that the accuracy of P nutrition prediction model of tomato was improved by combing spectral characteristics with physiological features. The LS-SVM model with IPSO can acquire better parameters than traditional LS-SVM model based on cross validation. The combination of spectral and physiological characteristics data with the proposed algorithm was proved to be a powerful diagnosis tool for P nutrition status in tomato, and provided a new idea for the rapid detection of tomato P nutrient content.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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