玉米叶片净光合速率快速检测方法研究
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摘要
玉米叶片的净光合速率可以用来表征植物生物量的积累和营养盈亏等健康状态,为探求玉米叶片净光合速率的快速无损检测方法,利用叶绿素荧光光谱分析技术对拔节期玉米叶片净光合速率进行检测。实验选取了吉林省典型种植品种先玉335作为研究对象,通过对80组数据的无量纲化处理和标准化处理,降低光谱噪声引起的样本差异,分析不同光谱波段与叶片净光合速率的相关性,确定500~550nm、675~715nm、715~745nm等3组波段作为光谱检测样本。选择675~715nm波段作为光谱波段的典型参数预测玉米叶片的净光合速率,得出两者之间存在显著线性关系,其决定系数R~2=0.7 9 2 4,表明以6 7 5~7 1 5 nm波段预测玉米叶片的净光合速率是可行的。对回归模型进行验证,得到预测值与真实值之间的决定系数R~2=0.7 9 2 1,表明此回归模型对拔节期玉米叶片净光合速率具有良好预测能力,为植物生理信息快速无损检测提供了新的方法。
The net photosynthetic rate of maize leaves can be used to characterize plant biomass accumulation and nutrient profit and loss. In order to explore the rapid non-destructive detection method of net photosynthetic rate of maize leaves,this study used the effects of chlorophyll fluorescence spectroscopy on maize leaf net photosynthetic rate for detection. A typical test selected plant varieties Jilin Xianyu335 as the research object. By analyzing the difference of spectral noise caused by the non-dimensional treatment and standardization of 80 groups of data,the correlation between different spectral bands and net photosynthetic rate was analyzed. Three bands of 500-550 nm,675-715 nm and 715-745 nm were selected as the samples. The net photosynthetic rate of maize leaves was predicted by selecting the 675-715 nm band as the typical parameters of the spectral band,and there was a significant linear relationship between them.The coefficient R~2= 0. 792,which was the first principal component of the 675-715 nm band. It was feasible to predict the net photosynthetic rate of maize leaves. The regression model was validated by the regression model,and the coefficient of correlation between the predicted value and the real value was R~2= 0. 7921,which indicated that the regression model had a good ability to predict the net photosynthetic rate of maize leaves at jointing stage and provide a theoretical basis for rapid non-destructive testing of plant physiological information.
The net photosynthetic rate of maize leaves can be used to characterize plant biomass accumulation and nutrient profit and loss. In order to explore the rapid non-destructive detection method of net photosynthetic rate of maize leaves,this study used the effects of chlorophyll fluorescence spectroscopy on maize leaf net photosynthetic rate for detection. A typical test selected plant varieties Jilin Xianyu335 as the research object. By analyzing the difference of spectral noise caused by the non-dimensional treatment and standardization of 80 groups of data,the correlation between different spectral bands and net photosynthetic rate was analyzed. Three bands of 500-550 nm,675-715 nm and 715-745 nm were selected as the samples. The net photosynthetic rate of maize leaves was predicted by selecting the 675-715 nm band as the typical parameters of the spectral band,and there was a significant linear relationship between them.The coefficient R~2= 0. 792,which was the first principal component of the 675-715 nm band. It was feasible to predict the net photosynthetic rate of maize leaves. The regression model was validated by the regression model,and the coefficient of correlation between the predicted value and the real value was R~2= 0. 7921,which indicated that the regression model had a good ability to predict the net photosynthetic rate of maize leaves at jointing stage and provide a theoretical basis for rapid non-destructive testing of plant physiological information.
引文
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[13]陈兵,王克如,李少昆,等.病害胁迫对棉叶光谱反射率和叶绿素荧光特性的影响[J].农业工程学报,2011,27(9):86-93.
[14]梁雪,吉海彦,王鹏新,等.用MSC-ANN方法建立冬小麦叶片叶绿素含量与反射光谱的定量分析模型[J].光谱学与光谱分析,2010,30(1):188-191.
[15]Ni Zhuoya,Liu Zhigang.Early water stress detection using leaf-level measurements of chlorophyll fluorescence and temperature data[J].Remote Sensing,2015,7(3):3232-3249.
[16]于海业,隋媛媛,杨昊谕,等.黄瓜叶片叶绿素荧光光谱日变化的研究[J].中国科学:信息科学,2010,40(S1):253-258.
[17]崔月,辛贵东,李文,等.不同类型玉米光合特性日变化的比较研究[J].吉林农业大学学报,2011,33(3):243-247.
[18]王琳琳,于海业,张蕾,等.基于叶绿素荧光光谱的生菜硝酸盐含量检测[J].农业工程学报,2016,32(14):279-283.
[2]Ndao A S,KontéA,Biaye M,et al.Analysis of chlorophyll fluorescence spectra in some tropical plants[J].Journal of Fluorescence,2005,15(2):123-129.
[3]王娣,佃袁勇,乐源,等.基于高光谱植被指数的叶片净光合速率Pn反演[J].地理与地理信息科学,2016,32(4):42-48.
[4]周欣,郭亚芬,魏永霞,等.水分处理对大豆叶片净光合速率、蒸腾速率及水分利用效率的影响[J].农业现代化研究,2007(3):374-376.
[5]Liu Liangyun,Zhang Yongjiang,Wang Jihua,et al.Detecting solar-induced chlorophyll fluorescence from field radiance spectra based on the fraunhofer line prince[J].IEEE Transaction on Geosciences and Remote sensing,2005,43(4):827-832.
[6]Van W S,Alonso Luis.Bidrectional sun-induced chlorophyll fluorescence emission is influenced by leaf structure and light scattering properties-A bottom-up approach[J].Remote Sensing of Environment,2015,158(1):169-179.
[7]Hendrickson L,Furbank R T,Chow W S.A simple alternative approach to assessing the fate of absorbed light energy using chlorophyll fluorescence[J].Photosynthesis Research,2004,82(1):73-81.
[8]Schreiber U,Bilger W,Neubauer C.Chlorophyll fluorescence as a non-destructive indicator for rapid assessment of in vivo photosynthesis[J].Ecological Studies,1994,100(1):49-70.
[9]隋媛媛.基于叶绿素荧光光谱分析的温室黄瓜病虫害预警方法[D].长春:吉林大学,2012.
[10]李璟.基于主动诱导叶绿素荧光的光谱分析方法的研究[D].西安:西安理工大学,2013.
[11]徐彬彬,石晓日,李德成,等.不同水肥条件下平方米的荧光光谱特征及其在遥感应用中的可能性[J].环境遥感,1992(7);51-59.
[12]隋媛媛,王庆钰,于海业.基于叶绿素荧光光谱指数的温室黄瓜病害预测[J].光谱学与光谱分析,2016,36(6):1779-1782.
[13]陈兵,王克如,李少昆,等.病害胁迫对棉叶光谱反射率和叶绿素荧光特性的影响[J].农业工程学报,2011,27(9):86-93.
[14]梁雪,吉海彦,王鹏新,等.用MSC-ANN方法建立冬小麦叶片叶绿素含量与反射光谱的定量分析模型[J].光谱学与光谱分析,2010,30(1):188-191.
[15]Ni Zhuoya,Liu Zhigang.Early water stress detection using leaf-level measurements of chlorophyll fluorescence and temperature data[J].Remote Sensing,2015,7(3):3232-3249.
[16]于海业,隋媛媛,杨昊谕,等.黄瓜叶片叶绿素荧光光谱日变化的研究[J].中国科学:信息科学,2010,40(S1):253-258.
[17]崔月,辛贵东,李文,等.不同类型玉米光合特性日变化的比较研究[J].吉林农业大学学报,2011,33(3):243-247.
[18]王琳琳,于海业,张蕾,等.基于叶绿素荧光光谱的生菜硝酸盐含量检测[J].农业工程学报,2016,32(14):279-283.