Yao Jieni, Sun Dawei, Cen Haiyan, Xu Haixia, Weng Haiyong, Yuan Fang, He Yong
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China.
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou, China.
Front Plant Sci. 2018 May 11;9:603. doi: 10.3389/fpls.2018.00603. eCollection 2018.
Plant responses to drought stress are complex due to various mechanisms of drought avoidance and tolerance to maintain growth. Traditional plant phenotyping methods are labor-intensive, time-consuming, and subjective. Plant phenotyping by integrating kinetic chlorophyll fluorescence with multicolor fluorescence imaging can acquire plant morphological, physiological, and pathological traits related to photosynthesis as well as its secondary metabolites, which will provide a new means to promote the progress of breeding for drought tolerant accessions and gain economic benefit for global agriculture production. Combination of kinetic chlorophyll fluorescence and multicolor fluorescence imaging proved to be efficient for the early detection of drought stress responses in the ecotype Col-0 and one of its most affected mutants called . Kinetic chlorophyll fluorescence curves were useful for understanding the drought tolerance mechanism of . Conventional fluorescence parameters provided qualitative information related to drought stress responses in different genotypes, and the corresponding images showed spatial heterogeneities of drought stress responses within the leaf and the canopy levels. Fluorescence parameters selected by sequential forward selection presented high correlations with physiological traits but not morphological traits. The optimal fluorescence traits combined with the support vector machine resulted in good classification accuracies of 93.3 and 99.1% for classifying the control plants from the drought-stressed ones with 3 and 7 days treatments, respectively. The results demonstrated that the combination of kinetic chlorophyll fluorescence and multicolor fluorescence imaging with the machine learning technique was capable of providing comprehensive information of drought stress effects on the photosynthesis and the secondary metabolisms. It is a promising phenotyping technique that allows early detection of plant drought stress.
由于植物具有多种避旱和耐旱机制以维持生长,其对干旱胁迫的响应较为复杂。传统的植物表型分析方法 labor-intensive,耗时且主观。将叶绿素动力学荧光与多色荧光成像相结合进行植物表型分析,可以获取与光合作用及其次生代谢产物相关的植物形态、生理和病理特征,这将为推动耐旱品种的育种进程和为全球农业生产带来经济效益提供新手段。叶绿素动力学荧光和多色荧光成像相结合被证明对于在生态型Col-0及其最受影响的突变体之一中早期检测干旱胁迫响应是有效的。叶绿素动力学荧光曲线有助于理解[具体突变体名称缺失]的耐旱机制。传统荧光参数提供了与不同基因型干旱胁迫响应相关的定性信息,相应图像显示了叶片和冠层水平上干旱胁迫响应的空间异质性。通过逐步向前选择法选择的荧光参数与生理性状高度相关,但与形态性状无关组合。结合支持向量机的最佳荧光特征分别对处理3天和7天的干旱胁迫植物与对照植物进行分类时准确率分别达到了93.3%和99.1%良好结果表明,叶绿素动力学荧光和多色荧光成像与机器学习技术相结合能够提供干旱胁迫对光合作用和次生代谢影响的全面信息。这是一种有前途的表型分析技术,能够早期检测植物干旱胁迫。