| 用于微小型光谱仪的冬小麦抽穗期叶绿素含量诊断模型 |
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| 引用本文： | 程萌,张俊逸,李民赞,刘豪杰,孙红,郑涛.用于微小型光谱仪的冬小麦抽穗期叶绿素含量诊断模型[J].农业工程学报,2017,33(Z1):157-163. |
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| 作者姓名： | 程萌 张俊逸 李民赞 刘豪杰 孙红 郑涛 |
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| 作者单位： | 1. 中国农业大学现代精细农业系统集成研究教育部重点实验室,北京,100083;2. 中国农业大学现代精细农业系统集成研究教育部重点实验室,北京 100083;河南牧业经济学院,郑州 450000 |
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| 基金项目： | 国家自然科学基金资助项目(31501219);"十三五"国家重点研发计划课题 (2016YFD0300606和2016YFD0200602) |
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| 摘 要： | 采用基于微小型光谱学传感器构建的作物冠层反射光谱探测系统,在田间轻量便携式地测量冬小麦抽穗期冠层可见光-近红外反射光谱。首先对冠层反射光谱进行去噪预处理,对原始光谱先一阶微分运算后,采用Bior Nr.Nd双正交小波进行小波包分解和重构以达到数据平滑的目的。对样本点数据利用蒙特卡罗抽样方法进行分析,去除异常样本点值,然后基于Random frog特征变量选取算法进行叶绿素含量敏感波长筛选。分别对原始光谱和经预处理后的光谱数据所选取的敏感波长进行偏最小二乘回归(partial least squares regression,PLSR)建模,建模结果如下:基于原始光谱的敏感波长639、436、459、642、556、653、596、455 nm建立叶绿素含量诊断PLSR模型,建模精度Rc2为0.70,均方根误差为1.398 0,验证精度Rv2为0.10,均方根误差为2.381 0;经过预处理后,基于选取的特征波长719、572、562、605、795、527、705、514 nm建立叶绿素含量诊断PLSR模型,建模精度Rc2为0.69,均方根误差为1.364 8,验证精度Rv2为0.52,均方根误差为1.839 7,估测能力得到了提高。结果表明:基于微小型光谱学传感器构建的作物冠层反射光谱探测系统能够合理地预测冬小麦叶片中的叶绿素含量,可用于田间冬小麦抽穗期的作物营养诊断。
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| 关 键 词： | 光谱分析 叶绿素 数据处理 RF算法 特征波长筛选 冬小麦穗期 |
| 收稿时间： | 2016/11/14 0:00:00 |
| 修稿时间： | 2017/1/12 0:00:00 |
Chlorophyll content diagnosis model of winter wheat at heading stage applied in miniature spectrometer |
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| Cheng Meng,Zhang Junyi,Li Minzan,Liu Haojie,Sun Hong and Zheng Tao.Chlorophyll content diagnosis model of winter wheat at heading stage applied in miniature spectrometer[J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(Z1):157-163. |
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| Authors: | Cheng Meng Zhang Junyi Li Minzan Liu Haojie Sun Hong and Zheng Tao |
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| Institution: | 1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China;,1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China; 2. Henan University of Animal Husbandry and Economy, Zhengzhou 450000, China;,1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China;,1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China;,1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China; and 1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China; |
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| Abstract: | Crop spectrum characteristic analysis plays an important role on identification of crops, production estimation, condition monitoring, and nutrition diagnosis crop management. In order to predict the nutrient content of crop non-destructively and quickly, a reflectance spectrum detection system for winter wheat was developed to measure the reflectance of 350-820 nm. The system has three parts: optical sensor, data storage and transmission module and the controller. The optical system was developed based on the Ocean Optics STS-VIS sensor. The sensor was controlled by an enhanced electronic device to obtain reflected light with a high-sensitivity 1024 pixel linear CCD array detector. The spectral information collection and analysis software was developed on Windows 7 platform, using PHP. Software included three modules: acquisition parameters, acquisition control and data management. After the controller and sensor connected successfully, users should optimize the system parameters, such as the integral time, the scan average times and boxcar width. Then it needed to choose the collection function, DN value. Finally, the reflectance data could be analyzed and stored. In order to test the performance of the spectrum analyzer, calibration experiment was carried out. A gray calibration board with four different gray gradations was involved. The correlation was analyzed between the spectral reflectance measured by spectrum analyzer and ASD Field Spec Hand Held 2. The result showed that the average correlation coefficient value was 0.94. it also showed that the developed spectrum analyzer was worked with good stability under different light conditions. The system was used to collect 350-820 nm (visible-near infrared) reflectance of winter wheat at heading stage in the field to detect the chlorophyll of winter wheat. In order to decrease the noise influence, canopy reflectance spectra curves were pre-processed by 1-order differential method. And the data smoothing was conducted by Bior Nr.Nd biorthogonal wavelet packet analysis method. After that, Monte Carlo sampling method was used to remove 5 outliers. And 8 sensitive wavelengths (719、572、562、605、795、527、705、514 nm) were chosen using Random frog algorithm. In order to indicate the effective of preprocessing method, the chlorophyll content detecting PLSR (partial least squares regression) model was established based on original reflectance spectra. The modeling determination coefficient was 0.70 and predictive determination coefficient was 0.10. Meanwhile, the revised chlorophyll content detecting model was established. The involved data was pre-processed after 1-order differential and wavelet packet decomposition. The modeling determination coefficient was 0.69. The root mean square error of model was 1.3648. And predictive determination coefficient was 0.52. The root mean square error of prediction was 1.8397. The modling results showed the background interference and noise of wheat canopy reflectance were removed effectively. The system based on miniature spectrometer could estimate the chlorophyll of wheat leaves and help to diagnose the crop nutrition of winter wheat at heading stage. |
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| Keywords: | spectrum analysis chlorophy II data processing RF algorithm characteristic wavelength selection wheat canopy |
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