| 基于高光谱成像的马铃薯叶片叶绿素分布可视化研究 |
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| 引用本文： | 郑涛,刘宁,孙红,龙耀威,杨玮,ZHANG Qin.基于高光谱成像的马铃薯叶片叶绿素分布可视化研究[J].农业机械学报,2017,48(S1):153-159， 340. |
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| 作者姓名： | 郑涛 刘宁 孙红 龙耀威 杨玮 ZHANG Qin |
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| 作者单位： | 中国农业大学,中国农业大学,中国农业大学,中国农业大学,中国农业大学,华盛顿州立大学 |
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| 基金项目： | 国家自然科学基金项目（31501219)、国家重点研发计划项目（2016YFD0300606、2016YFD0300610）和中央高校基本科研业务费专项资金项目（2017TC029） |
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| 摘 要： | 针对马铃薯作物叶片进行了叶绿素含量无损检测技术及分布图绘制方法研究，用以指示作物长势并指导精细化管理。首先利用高光谱成像技术采集了65个马铃薯叶片的400个样本点高光谱图像和相应的SPAD值，提取并计算叶绿素测量区域的叶片平均光谱后，分别采用蒙特卡罗无信息变量消除算法（MC-UVE）和自适应重加权算法（CARS）筛选出了12个和23个叶绿素含量敏感波长，建立了马铃薯叶片叶绿素含量偏最小二乘（PLS）回归模型。建模结果如下：基于MC-UVE算法筛选的12个敏感波长的PLSR诊断模型，建模精度R2C为0.79，验证精度R2V为0.73；基于CARS算法筛选的23个敏感波长建立的PLSR诊断模型，建模精度R2C为0.82，验证精度R2V为0.80。择优选取CARS-PLSR模型计算马铃薯叶片每个像素点的叶绿素含量，从而利用伪彩色绘图绘制了马铃薯叶片叶绿素含量可视化分布图，最终实现马铃薯叶片含量无损检测以及叶绿素分布可视化表达，以期为后续马铃薯作物大田冠层叶绿素分布诊断提供支持。
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| 关 键 词： | 叶绿素含量 马铃薯叶片 蒙特卡罗无信息变量消除算法 自适应重加权算法 高光谱成像 |
| 收稿时间： | 2017/7/10 0:00:00 |
Visualization of Chlorophyll Distribution of Potato Leaves Based on Hyperspectral Imaging Technology |
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| ZHENG Tao,LIU Ning,SUN Hong,LONG Yaowei,YANG Wei and ZHANG Qin.Visualization of Chlorophyll Distribution of Potato Leaves Based on Hyperspectral Imaging Technology[J].Transactions of the Chinese Society of Agricultural Machinery,2017,48(S1):153-159， 340. |
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| Authors: | ZHENG Tao LIU Ning SUN Hong LONG Yaowei YANG Wei and ZHANG Qin |
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| Institution: | China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University and Washington State University |
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| Abstract: | Non-destructive detection of chlorophyll content and drawing the chlorophyll distribution map of potato crop leaves could indicate crop growth and guide field management. In this paper, the hyperspectral imaging technique was used to diagnose the chlorophyll content index and help to describe chlorophyll distribution of potato leaf. The hyperspectral images of 65 potato leaves were collected and divided into 400 regions of interesting (ROI). Meanwhile, the SPAD values of these 400 ROI samples were measured. After extracting and calculating the average leaf spectrum of the chlorophyll measurement area, the 12 chlorophyll content sensitive wavelengths were chosen by the Monte Carlo uninformative variables elimination (MC-UVE) algorithm and the 23 chlorophyll content sensitive wavelengths were selected by the competitive adaptive reweighted sampling (CARS) algorithm. They were used to establish the partial least squares regression (PLSR) model of chlorophyll content index of potato leaves respectively. The results were as follows: 12 sensitive wavelengths selected by MC-UVE algorithm were 532.54nm, 534.27nm, 566.78nm, 737.60nm, 741.61nm, 742.51nm, 759.49nm, 772.92nm, 816.54nm, 880.88nm, 928.84nm, 943.88nm. The modeling determination coefficient was 0.79, and predictive determination coefficient was 0.73. Meanwhile, 23 sensitive wavelengths selected by the CARS algorithm were 394.01nm, 399.94nm, 492.03nm, 493.32nm, 494.18nm, 534.27nm, 536.86nm, 537.30nm, 537.73nm, 543.79nm, 544.22nm, 545.52nm, 547.25nm, 547.69nm, 548.12nm, 550.29nm, 550.72nm, 553.76nm, 555.49nm, 938.93nm, 986.36nm, 987.74nm, 1018.30nm.The modeling determination coefficient of the PLSR diagnostic model built with these wavelengths was 0.82, and predictive determination coefficient was 0.80. Thus, the chlorophyll content of potato leaves can be calculated by CARS-PLS model, and the visual distribution map of chlorophyll content in potato leaves was plotted by using pseudo-color drawing. It provides a method for the diagnosis of chlorophyll distribution in the future. |
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| Keywords: | chlorophyll content potato leaves MC-UVE CARS hyperspectral imaging |
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