丁永军,张晶晶,李修华,李民赞.基于光谱红边位置提取算法的番茄叶片叶绿素含量估测[J].农业机械学报,2016,47(3):292-297.
Ding Yongjun,Zhang Jingjing,Li Xiuhua,Li Minzan.Estimation of Chlorophyll Content of Tomato Leaf Using Spectrum Red Edge Position Extraction Algorithm[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(3):292-297.
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基于光谱红边位置提取算法的番茄叶片叶绿素含量估测   [下载全文]
Estimation of Chlorophyll Content of Tomato Leaf Using Spectrum Red Edge Position Extraction Algorithm   [Download Pdf][in English]
投稿时间:2015-07-18  
DOI:10.6041/j.issn.1000-1298.2016.03.041
中文关键词:  番茄叶片  叶绿素含量  光谱分析; 红边位置
基金项目:国家自然科学基金项目(31360291,31401290)和甘肃省高等学校科研基金项目(2013B-071)
作者单位
丁永军 兰州城市学院 
张晶晶 兰州城市学院 
李修华 广西大学 
李民赞 中国农业大学 
中文摘要:为了快速、准确估测番茄叶片叶绿素含量,分析了不同营养水平下的番茄叶片光谱红边参数变化规律,发现红边位置最能表征番茄叶绿素状况,统计分析了6种算法提取的光谱红边位置的差异性,并为每种算法分别建立了5种估测模型,验证结果表明每种红边位置提取算法所对应的最佳模型为线性四点内插法的指数曲线模型和其他红边位置算法的对数曲线模型。其中线性外推法模型精度最高,校正集决定系数R2c为0.6186,验证集决定系数R2v达到07711,验证集均方根误差RMSEv为83596,可以有效诊断番茄叶绿素含量。线性四点内插法根据670、700、740、780nm 4个波段的叶片反射率计算红边位置,运算简单,模型精度较高,R2c为0.6217,R2v达到0.7666,RMSEv为8.5682,可以作为开发番茄叶绿素含量监测仪器的依据。
Ding Yongjun  Zhang Jingjing  Li Xiuhua  Li Minzan
Lanzhou City University,Lanzhou City University,Guangxi University and China Agricultural University
Key Words:tomato leaf  chlorophyll content  spectral analysis  red edge position
Abstract:The red edge parameters of plants spectrum were used to estimate foliar chlorophyll for nitrogen content and leaf area. Among these parameters, the red edge position (REP) is the best one for diagnosing the growth state of tomato according to statistical analysis. The REP was defined by the wavelength of the maximum first derivative of the reflectance spectrum in the region (660nm to 780nm) of the red edge. The six algorithms could be used to extract the REP, including four point interpolation, maximum first derivative, inverted Gaussian fitting, Lagrangian, linear extrapolation, and polynomial fitting. In order to achieve a rapid and accurate application for predicting the chlorophyll content of tomato with REP, this study systematically analyzed the quantitative relationships and statistical characters between REP on various algorithms and leaf chlorophyll status, and then the linear regression, logarithmic regression, power regression, exponential regression and quadratic polynomial regression were used to develop the prediction models of the chlorophyll content for each REP extraction algorithm. The result showed that the logarithmic model of the linear extrapolation had the best accuracy and reliability. The calibration R2c was 0.6186, the validation R2v was 0.7711 and the root mean squared error of validation set (RMSv) was 8.3596. The exponential model of the four point interpolation could be obtained easily according to reflectance at 670nm, 700nm, 740nm and 780nm, the calibration R2c was 0.6217, validation R2v was 0.7666 and RMSEv was 8.5682. The predictive ability was good enough to develop a monitoring instrument of tomato chlorophyll 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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