首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity
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High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity

机译:使用高光谱反射率和局部最小二乘回归(PLSR)的高通量场表型揭示了对光合容量的遗传修改

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Spectroscopy is becoming an increasingly powerful tool to alleviate the challenges of traditional measurements of key plant traits at the leaf, canopy, and ecosystem scales. Spectroscopic methods often rely on statistical approaches to reduce data redundancy and enhance useful prediction of physiological traits. Given the mechanistic uncertainty of spectroscopic techniques, genetic modification of plant biochemical pathways may affect reflectance spectra causing predictive models to lose power. The objectives of this research were to assess over two separate years, whether a predictive model can represent natural and imposed variation in leaf photosynthetic potential for different crop cultivars and genetically modified plants, to assess the interannual capabilities of a partial least square regression (PLSR) model, and to determine whether leaf N is a dominant driver of photosynthesis in PLSR models. In 2016, a PLSR analysis of reflectance spectra coupled with gas exchange data was used to build predictive models for photosynthetic parameters including maximum carboxylation rate of Rubisco (V-c,V-max), maximum electron transport rate (J(max)) and percentage leaf nitrogen ([N]). The model was developed for wild type and genetically modified plants that represent a wide range of photosynthetic capacities. Results show that hyperspectral reflectance accurately predicted V-c,V-max,V- J(max) and [N] for all plants measured in 2016. Applying these PLSR models to plants grown in 2017 resulted in a strong predictive ability relative to gas exchange measurements for V-c,V-max but not for J(max,) and not for genotypes unique to 2017. Building a new model including data collected in 2017 resulted in more robust predictions, with R-2 increases of 17% for V-c,V-max. and 13% Plants generally have a positive correlation between leaf nitrogen and photosynthesis, however, tobacco with reduced Rubisco (SSuD) had significantly higher [N] despite much lower V-c,V-max. The PLSR model w
机译:光谱学正成为一种越来越强大的工具,可以缓解叶子,冠层和生态系统尺度的关键植物特征的传统测量的挑战。光谱方法通常依赖于统计方法来降低数据冗余,增强生理性状的有用预测。鉴于光谱技术的机械不确定性,植物生化途径的遗传修饰可能影响反射光谱,导致预测模型失去动力。该研究的目标是评估两个单独的几年,预测模型是否可以代表不同作物品种和遗传修饰植物的叶片光合潜力的自然和施加变化,以评估偏最小二乘回归的持续能力(PLSR)模型,并确定叶N是否是PLSR模型中光合作用的主要驱动因子。 2016年,使用与气体交换数据相结合的反射光谱的PLSR分析来构建用于光合参数的预测模型,包括Rubisco(VC,V-MAX),最大电子传输速率(J(MAX))和百分比叶片的最大羧化速率氮气([n])。该模型是为野生型和遗传修饰植物开发的,代表着广泛的光合容量。结果表明,对于2016年测量的所有植物,高光谱反射率准确地预测了VC,V-MAX,V-J(MAX)和[N]。将这些PLSR模型应用于2017年生长的植物产生了相对于气体交换测量的强烈预测能力对于VC,V-MAX但不是J(MAX,)而不是2017年独一无二的基因型。构建包括2017年收集的数据的新模型导致更强大的预测,R-2增加了17%的VC,V-最大限度。并且13%的植物通常具有叶状氮和光合作用之间的正相关,然而,尽管V-C的V-C,V-Max大得多,但烟草具有降低的Rubisco(SSUD)显着更高。 PLSR模型W.

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