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Predicting sugarcane physiological traits using hyperspectral reflectance

机译：预测使用高光谱反射的甘蔗生理性状

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摘要

Physiological traits have the potential to accelerate genetic improvement for adaptation to abiotic stresses, resource-use efficiency, and yield. However, using these traits as selection targets in breeding programs is constrained by current phenotyping approaches that involve destructive, time-consuming, and labor-intensive measurements. There is growing interest in developing high-throughput tools and prediction models for the precise phenotyping of important physiological traits under field conditions. The aim of this study was to explore the potential of remotely piloted aircraft (RPA)-based canopy hyperspectral reflectance for predicting physiological and biochemical traits in sugarcane. Canopy hyperspectral reflectance in the 4001700 nm spectral region was collected from 10 genotypes grown under three nitrogen (N) treatments under field conditions. Simultaneously, leaf-level physiological and biochemical traits such as photosynthesis, sucrose, and starch content were measured to develop partial least squares (PLS) prediction models. Canonical powered partial least moderated accuracy (R-2 = -0.6). Partial least square regression (PLSR) models for predicting physiological, biochemical, and yield traits from hyperspectral data had varying degrees of accuracy. The prediction accuracy was good for cane yield and sugar yield (R-2 = similar to 0.5), moderated for leaf sucrose, leaf starch content, and gas exchange attributes (R-2 = similar to 0.2), while it was poor for the other traits. It appears that a larger spectral and trait dataset from measurements made under different environmental conditions and crop growth stages is needed to improve the PLS prediction model. The results of this initial proof-of-concept study demonstrates the effectiveness of hyperspectral sensing for characterising and predicting certain physiological and yield attributes. Validation of these results across seasons and under distinct environmental conditions using diverse gentoypes is needed before delivering prediction models for phentoyping sugarcane physiological traits using hyperspectral reflectance.
机译：生理性状有可能加速遗传改善，以适应非生物应激，资源利用效率和产量。然而，使用这些特性作为育种计划中的选择目标受到涉及破坏性，耗时和劳动密集型测量的当前表型方法的约束。在现场条件下开发高通量工具和预测模型的高吞吐工具和预测模型越来越感兴趣。本研究的目的是探讨远程飞机（RPA）的潜在的冠层高光谱反射率，以预测甘蔗中的生理生化性状。从在现场条件下，从在三个氮气（n）处理下生长的10种基因型中收集4001700nm光谱区域中的冠层高光谱反射率。同时，测量叶级生理和生化特征，如光合作用，蔗糖和淀粉含量，以发育偏最小二乘（PLS）预测模型。规范动力部分最小化学精度（R-2 = -0.6）。用于预测来自高光谱数据的生理，生物化学和产量特征的局部最小二乘回归（PLSR）模型具有不同程度的准确度。预测精度适用于甘蔗产量和糖产率（R-2 =类似于0.5），适用于叶片蔗糖，叶淀粉含量和气体交换属性（R-2 =类似于0.2），而这是穷人其他特征。看起来需要在不同环境条件和作物生长阶段进行的测量的较大光谱和特征数据集来改善PLS预测模型。这种初始概念验证研究的结果证明了高光谱感测表征和预测某些生理和产量属性的有效性。在使用高光谱反射率为Phentoyping甘蔗生理特性提供预测模型之前，需要验证这些季节的结果以及使用不同的牙胶的不同环境条件。

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  • 来源
    《International Sugar Journal》 |2021年第1474期|690-697|共8页
  • 作者

    Natarajan S.; Deutschenbaur J.; Basnayake J.; Lakshmanan P.;

  • 作者单位

    Sugar Res Australia Ltd Brandon Qld 4808 Australia;

    Sugar Res Australia Ltd Brandon Qld 4808 Australia;

    Sugar Res Australia Ltd Brandon Qld 4808 Australia;

    Guangxi Acad Agr Sci Sugarcane Res Inst Nanning 2021 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
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