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A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform

文献类型： 外文期刊

作者： Hassan, Muhammad Adeel 1 ; Yang, Mengjiao 1 ; Rasheed, Awais 1 ; Yang, Guijun 3 ; Reynolds, Matthew 4 ; Xia, Xianch 1 ;

作者机构： 1.Chinese Acad Agr Sci, Natl Wheat Improvement Ctr, Inst Crop Sci, Beijing 100081, Peoples R China

2.Xinjiang Agr Univ, Coll Agron, Urumqi 830052, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China

4.Int Maize & Wheat Improvement Ctr CIMMYT, Global Wheat Program, Apdo Postal 6-641, Mexico City 06600, DF, Mexico

5.CAAS, Int Maize & Wheat Improvement Ctr CIMMYT China Of, Beijing 100081, Peoples R China

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

期刊名称：PLANT SCIENCE （ 影响因子：4.729； 五年影响因子：5.132 ）

ISSN： 0168-9452

年卷期： 2019 年 282 卷

页码：

收录情况： SCI

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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