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孙红,郑涛,刘宁,程萌,李民赞,Zhang Qin.高光谱图像检测马铃薯植株叶绿素含量垂直分布[J].农业工程学报,2018,34(1):149-156.DOI:10.11975/j.issn.1002-6819.2018.01.020
高光谱图像检测马铃薯植株叶绿素含量垂直分布
投稿时间:2017-08-01  修订日期:2017-11-09
中文关键词:  光谱分析  作物  叶绿素  垂直分布  马铃薯作物  随机蛙跳算法  高光谱成像
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 (2016YFD0300606,2016YFD0200602)。
作者单位
孙红 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
 
郑涛 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
 
刘宁 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
 
程萌 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
 
李民赞 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
 
Zhang Qin 2. 美国华盛顿州立大学精细农业及农业自动化研究中心WA99350 
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中文摘要:为了检测马铃薯作物叶绿素含量,该文按照叶片垂直分布位置采集马铃薯叶片样本的成像高光谱数据,提取并计算了400个划分区域的平均光谱,使用手持式SPAD-502叶绿素仪测定了相应位置的SPAD(soil plant analysis development)值。采用标准正态变量校正(standard normal variate,SNV)方法对光谱数据进行预处理,分析了开花期植株自下而上垂直叶位间光谱和叶绿素分布关系,其光谱反射率在382~700 nm区间随叶位的升高反射率增加(上>中>下),在700~1 019 nm范围下叶位反射率高于上部和中部叶位(下>上>中),且SPAD均值依次为36.41、43.11、47.04。分别采用相关系数分析法和随机蛙跳(random frog,RF)算法筛选叶绿素含量敏感波长,并建立偏最小二乘回归(partial least squares regression,PLSR)模型。结果如下:基于相关系数分析法筛选的12个敏感波长主要位于530~550和706~708nm范围,建模精度RC2为0.7 588,验证精度RV2为0.5 773;基于random frog算法筛选的11个敏感波长(554.62、560.26、575.04、576.35、595.09、604.7、649.44、731.8、752.78、786.38、789.97 nm),建模精度RC2为0.8 423,验证精度RV2为0.7 676。选取RF-PLS模型计算马铃薯叶片每个像素点的叶绿素含量,绘制不同叶位马铃薯叶片叶绿素含量可视化分布图,结果可反映马铃薯在开花期植株上叶片叶绿素动态响应关系,实现了不同叶位马铃薯叶片叶绿素含量无损检测以及分布可视化表达。
Sun Hong,Zheng Tao,Liu Ning,Cheng Meng,Li Minzan,Zhang Qin.Vertical distribution of chlorophyll in potato plants based on hyperspectral imaging[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2018,34(1):149-156.DOI:10.11975/j.issn.1002-6819.2018.01.020
Vertical distribution of chlorophyll in potato plants based on hyperspectral imaging
Author NameAffiliation
Sun Hong 1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
 
Zheng Tao 1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
 
Liu Ning 1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
 
Cheng Meng 1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
 
Li Minzan 1. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
 
Zhang Qin 2. Center for Precision & Automated Agricultural System, Washington State University, Prosser, WA 99350, USA
 
Key words:spectrum analysis  crops  chlorophyll  vertical distribution  potato plant  random frog algorithm  hyperspectral imaging
Abstract: To monitor the potato growth status, the chlorophyll content was involved and its vertical distribution was detected at different leaf positions in plants. In this paper, the experiment was conducted during flowering stage of potato in the greenhouse of School of Water Conservancy and Engineering, China Agricultural University in June 2017. The potato leaf samples were randomly collected at 3 leaf positions (upper, middle, lower) of different potato plants. Then the hyper-spectral data of 66 potato leaf samples were divided into 400 regions of interesting (ROIs) and the SPAD (soil plant analysis development) values of the corresponding leaf positions were measured, of which 140 were taken from the upper part of the leaves and both 130 from the middle and lower leaves. The chlorophyll distribution was analyzed based on the vertical leaf position. The results indicated that its spectral reflectance in the range of 382-700 nm increased with the rise of the position. In the range of 700-1019 nm, the reflectance at the lower leaf position was higher than that at the upper and middle leaves, and the mean values of SPAD were 36.41, 43.11, and 47.04, respectively, at lower, upper and middle position. The black and white corrections of the potato leaf sample images collected using the hyperspectral imaging system were performed. After extracting and calculating the average leaf spectrum of the chlorophyll measurement area, the spectral data were pretreated by the standard normal variable (SNV) correction method, and then 2 sensitive wavelength selection methods were applied to built the chlorophyll content estimation models. The 12 wavelengths were chosen by using the correlation coefficient (CC) analysis and the 11 wavelengths were selected by the random frog (RF) algorithm. The results from the partial least squares regression (PLSR) model showed that 12 sensitive wavelengths selected by the CC analysis method were mainly located in the range of 530-550 and 706-708 nm. And in the PLSR, the modeling determination coefficient was 0.7588, and the predictive determination coefficient was 0.5773. Meanwhile, based on the RF algorithm, 11 sensitive wavelengths were 554.62, 560.26, 575.04, 576.35, 595.09, 604.7, 649.44, 731.8, 752.78, 786.38, and 789.97 nm. The modeling determination coefficient of the PLSR prediction model built with these wavelengths was 0.842 3, and the predictive determination coefficient was 0.767 6. Thus, the chlorophyll content of each leaf of potato was calculated by RF-PLS model, and the visual distribution of chlorophyll content in potato leaves was plotted. The results showed that the hyperspectral imaging could reflect the dynamic response of potato chlorophyll in flowering stage and achieve the non-destructive detection of potato leaf chlorophyll content and the visual expression of chlorophyll distribution.
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