数字农科院2.0

A Rapid Monitoring Of Ndvi A.cross The Wheat Growth C ycle For Grain Yield Prediction Using A Multi-Spectral Uav Platform

文献类型： 外文期刊

作者： Hassan, MA; Yang, MJ; Rasheed, A; Yang, GJ; Reynolds, M; Xia, XC; Xiao, YG; He, ZH

作者机构：

关键词： High throughput phenotyping; Multi-spectral imaging; Normalized difference vegetation index; Unmanned aerial vehicle

期刊名称： PLANT SCIENCE

ISSN： 0168-9452

年卷期： 2019 年 282 卷

页码：

收录情况： JCR（2021版）

摘要： Wheat improvement programs require rapid assessment of large numbers of individual plots across multiple environments. Vegetation indices (VIs) that are mainly associated with yield and yield-related physiological traits, and rapid evaluation of canopy normalized difference vegetation index (NDVI) can assist in-season selection. Multi-spectral imagery using unmanned aerial vehicles (UAV) can readily assess the VIs traits at various crop growth stages. Thirty-two wheat cultivars and breeding lines grown in limited irrigation and full irrigation treatments were investigated to monitor NDVI across the growth cycle using a Sequoia sensor mounted on a UAV. Significant correlations ranging from R-2 = 0.38 to 0.90 were observed between NDVI detected from UAV and Greenseeker (GS) during stem elongation (SE) to late grain gilling (LGF) across the treatments. UAV-NDVI also had high heritabilities at SE (h(2) = 0.91), flowering (F) (h(2) = 0.95), EGF (h(2) = 0.79) and mid grain filling (MGF) (h(2) = 0.71) under the full irrigation treatment, and at booting (B) (h(2) = 0.89), EGF (h(2) = 0.75) in the limited irrigation treatment. UAV-NDVI explained significant variation in grain yield (GY) at EGF (R-2 = 0.86), MGF (R-2 = 0.83) and LGF (R-2 = 0.89) stages, and results were consistent with GS-NDVI. Higher correlations between UAV-NDVI and GY were observed under full irrigation at three different grain-filling stages (R-2 = 0.40, 0.49 and 0.45) than the limited irrigation treatment (R-2 = 0.08, 0.12 and 0.14) and GY was calculated to be 24.4% lower under limited irrigation conditions. Pearson correlations between UAV-NDVI and GY were also low ranging from r = 0.29 to 0.37 during grain-filling under limited irrigation but higher than GS-NDVI data. A similar pattern was observed for normalized difference red-edge (NDRE) and normalized green red difference index (NGRDI) when correlated with GY. Fresh biomass estimated at late flowering stage had significant correlations of r = 0.30 to 0.51 with UAV-NDVI at EGF. Some genotypes Nongda 211, Nongda 5181, Zhongmai 175 and Zhongmai 12 were identified as high yielding genotypes using NDVI during grain-filling. In conclusion, a multispectral sensor mounted on a UAV is a reliable high-throughput platform for NDVI measurement to predict biomass and GY and grain filling stage seems the best period for selection.

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