文献类型： 外文期刊

作者： Natarajan, S. ¹ ; Deutschenbaur, J. ¹ ; Basnayake, J. ¹ ; Lakshmanan, P. ² ;

作者机构： 1.Sugar Res Australia Ltd, Brandon, Qld 4808, Australia

2.Guangxi Acad Agr Sci, Sugarcane Res Inst, Nanning 2021, Peoples R China

期刊名称：INTERNATIONAL SUGAR JOURNAL （ 影响因子：0.119； 五年影响因子：0.145 ）

ISSN： 0020-8841

年卷期： 2021 年 123 卷 1474 期

页码：

收录情况： SCI

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.