LED补光对弱光下番茄幼苗叶绿素荧光特性的影响  

Effect of LED Supplementary Light on Chlorophyll Fluorescence Characteristics of Tomato Seedlings under Low Light

作　　者：纪建伟[1] 李金鹏 董贞芬 李征明[1] 刘珍珍 高湘荃 缪新颖 LI Jin-peng;JI Jian-wei;DONG Zhen-fen;LI Zheng-ming;LIU Zhen-zhen;GAO Xiang-quan;MIAO Xin-ying(College of information and electrical engineering,Shenyang Agricultural University,Shenyang 110161,China;School of Information Engineering,Dalian Ocean University,Dalian Liaoning 116023,China)

机构地区：[1]沈阳农业大学信息与电气工程学院,沈阳110161 [2]大连海洋大学信息工程学院,辽宁大连116023

出　　处：《沈阳农业大学学报》2021年第2期232-238,共7页Journal of Shenyang Agricultural University

基　　金：辽宁省高等学校重点项目(01122017003);辽宁省自然科学基金重点项目(20170540797)。

摘　　要：针对番茄幼苗受弱光胁迫后以及补光恢复情况的识别,选用辽园多丽、园艺L-404和草莓番茄幼苗作为试验对象。通过人工模拟温室弱光环境以及后期补光,使用IMAGING-PAM叶绿素荧光仪对不同品种番茄在弱光环境下和恢复期的荧光参数进行检测并获取叶绿素荧光参数图像,筛选番茄叶片光敏感性有用参数;将有用参数的叶绿素荧光图像特的征值提取,获得以R、G、B、L、a、b为特征值的样本;采用BP神经网络建立一种对补光恢复识别的模型,使用trainglm函数,经过建模和训练后获取对弱光胁迫后补光恢复的识别情况。结果表明:在叶绿素荧光参数上,不同品种的番茄幼苗受弱光胁迫后,F、Fm'、Fo'、Y(NO)、qN参数均无明显变化;Y(NPQ)、NPQ参数有上升趋势;Y(II)、qP、qL呈下降趋势;在图像上F、Y(NO)、Fo'、Fm'、qN上无明显冷害区域,而Y(II)、Y(NPQ)、NPQ、qP、qL这几个参数的图像上冷害区域明显。补光后各参数以及叶绿素荧光图像上的冷害区域均缓慢的恢复正常。在对比各参数的荧光图像后,建议使用Y(II)用于评估弱光胁迫番茄叶片的损伤和补光的恢复。样本经BP神经网络建立的补光恢复识别模型,训练后对样本识别率达到87.671%,总体准确率R=98.9%,均方误差为u=0.11421,具有一定准确性,对补光恢复进行对进一步研究植物生长受环境胁迫后的影响提供参考。For the identification of tomato seedlings under low light stress and the recovery of supplementary light,Liaoyuan Duoli,Yuanyi L-404 and strawberry tomato seedlings were selected as experimental objects.Through artificial simulation of the low-light environment of greenhouse and the supplementary light in the later period,the chlorophyll fluorescence analyzer was used to detect the fluorescence parameters of different tomato varieties in the low-light environment and recovery period and to obtain the chlorophyll fluorescence parameter images,so as to screen the photosensitive useful parameters of tomato leaves.The characteristic values of chlorophyll fluorescence images with useful parameters were extracted to obtain samples with R,G,B,L,a,b as characteristic values.BP neural network was used to establish a model for the recovery recognition of supplementary light.Trainglm function was used to obtain the recognition of supplementary light recovery after low light stress after modeling and training.The results showed that F,Fm',Fo',Y(NO)and qN parameters of tomato seedlings of different varieties had no significant changes under low light stress.Y(NPQ)and NPQ have an upward trend.Y(II),qP and qL showed a downward trend.In the image,there is NO obvious chilling injury region on F,Y(NO),Fo',Fm'and qN,while there is obvious chilling injury region on the image of Y(II),Y(NPQ),NPQ,qP and qL.All the parameters and the cold damage areas on the chlorophyll fluorescence images were restored to normal slowly after light supplemening.After comparing the fluorescence images of various parameters,Y(II)was recommended to evaluate the damage of tomato leaves under low light stress and the recovery of supplementary light.After the training,the identification rate of the sample was 87.671%,the overall accuracy was R=98.9%,and the mean square error was u=0.11421,which had certain accuracy.It could provide reference for further research on the influence of the supplemented light recovery on plant growth under environmental stress.

关 键 词：叶绿素 荧光成像 弱光胁迫 补光恢复 BP神经网络 

分 类 号：S641.2[农业科学—蔬菜学] TP183[农业科学—园艺学]