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High-Throughput Field Phenotyping of Leaves Leaf Sheaths Culms and Ears of Spring Barley Cultivars at Anthesis and Dough Ripeness

机译:花期和面团成熟期大麦品种叶片叶鞘茎秆和穗的高通量表型

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摘要

To optimize plant architecture (e.g., photosynthetic active leaf area, leaf-stem ratio), plant physiologists and plant breeders rely on destructively and tediously harvested biomass samples. A fast and non-destructive method for obtaining information about different plant organs could be vehicle-based spectral proximal sensing. In this 3-year study, the mobile phenotyping platform PhenoTrac 4 was used to compare the measurements from active and passive spectral proximal sensors of leaves, leaf sheaths, culms and ears of 34 spring barley cultivars at anthesis and dough ripeness. Published vegetation indices (VI), partial least square regression (PLSR) models and contour map analysis were compared to assess these traits. Contour maps are matrices consisting of coefficients of determination for all of the binary combinations of wavelengths and the biomass parameters. The PLSR models of leaves, leaf sheaths and culms showed strong correlations (R2 = 0.61–0.76). Published vegetation indices depicted similar coefficients of determination; however, their RMSEs were higher. No wavelength combination could be found by the contour map analysis to improve the results of the PLSR or published VIs. The best results were obtained for the dry weight and N uptake of leaves and culms. The PLSR models yielded satisfactory relationships for leaf sheaths at anthesis (R2 = 0.69), whereas only a low performance for all of sensors and methods was observed at dough ripeness. No relationships with ears were observed. Active and passive sensors performed comparably, with slight advantages observed for the passive spectrometer. The results indicate that tractor-based proximal sensing in combination with optimized spectral indices or PLSR models may represent a suitable tool for plant breeders to assess relevant morphological traits, allowing for a better understanding of plant architecture, which is closely linked to the physiological performance. Further validation of PLSR models is required in independent studies. Organ specific phenotyping represents a first step toward breeding by design.
机译:为了优化植物结构(例如,光合作用的有效叶面积,叶干比),植物生理学家和植物育种者依赖于破坏性且乏味的生物质样品。一种用于获取有关不同植物器官信息的快速且无损的方法可以是基于车辆的光谱近端传感。在这项为期3年的研究中,使用移动表型平台PhenoTrac 4比较了34个春季大麦品种在花期和面团成熟时主动和被动光谱近端传感器对叶片,叶鞘,茎秆和穗部的测量。比较已发表的植被指数(VI),偏最小二乘回归(PLSR)模型和等高线图分析来评估这些特征。等高线图是由波长和生物量参数的所有二进制组合的确定系数组成的矩阵。叶片,叶鞘和茎的PLSR模型显示出很强的相关性(R 2 = 0.61-0.76)。公布的植被指数描绘了类似的确定系数;但是,它们的RMSE较高。轮廓图分析未发现任何波长组合可改善PLSR或已发布VI的结果。叶片和茎的干重和氮吸收量获得了最佳结果。 PLSR模型在花期对叶鞘产生令人满意的关系(R 2 = 0.69),而在面团成熟时,所有传感器和方法的性能均较低。没有观察到与耳朵的关系。有源和无源传感器的性能相当,无源光谱仪具有一些优势。结果表明,基于拖拉机的近端传感技术与优化的光谱指数或PLSR模型相结合,可能代表植物育种者评估相关形态特征的合适工具,从而可以更好地了解与生理性能密切相关的植物结构。独立研究需要进一步验证PLSR模型。器官特异性表型代表了设计繁殖的第一步。

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