Early estimation of glutelin to gliadin ratio in wheat grain using high-dimensional and hyperspectral reflectance
文献类型： 外文期刊
作者： Ma, Junjie 1 ; Wang, Keyi 1 ; Zheng, Bangyou 2 ; Xu, Yinlong 1 ; He, Yong 1 ;
作者机构： 1.Chinese Acad Agr Sci, Inst Environm & Sustainable Dev Agr, Beijing, Peoples R China
2.CSIRO, Agr & Food, Queensland Biosci Precinct, St Lucia, Qld 4067, Australia
关键词： Protein components; Crop phenotypic monitoring; Precise estimation; Grain quality; Machine learning
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）
ISSN： 0168-1699
年卷期： 2024 年 227 卷
页码：
收录情况： SCI
摘要： Precise and timely estimation of the glutelin-to-gliadin ratio (Glu/Gli) in wheat grain is pivotal for crop monitoring, as it is a crucial quality indicator ensuring the production of high-quality wheat flour. Despite the recognized potential of hyperspectral technology in crop phenotype estimation, its application to estimate Glu/ Gli in wheat grains faces challenges due to complex spectral-chemical relationships and the influence of growing seasons. This study addresses this gap by cultivating 11 wheat varieties and collecting high-dimensional hyperspectral data from field experiments during various growth stages of wheat (2018-2019 and 2019-2020). Utilizing vegetation indices (VIs) in conjunction with linear mixed-effects model (LMM) and random forest regression model (RFR), it constructs a robust Glu/Gli estimation model (with a Glu/Gli range of 1.063 to 2.218). Results reveal that a singular VI application suffers from data limitations, while the integration of multiple VIs significantly enhances estimation accuracy. The mid-grain filling period emerges as a critical stage for accurate Glu/Gli estimation, with TCARI (transformed chlorophyll absorption reflectance index) demonstrating notable significance and high correlation. In model performance, RFR (R2 = 0.691, rRMSE = 0.096, RPD = 1.872, RER = 6.028) outperforms LMM (R2 = 0.477, rRMSE = 0.131, RPD = 1.383, RER = 4.453), exhibiting superior accuracy in estimating grain Glu/Gli for diverse wheat varieties. This study introduces a rapid and accurate approach for early wheat grain Glu/Gli estimation, offering valuable insights for wheat value chain and precision farming.
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