Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China.
Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China; College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China.
Plant Sci. 2019 May;282:95-103. doi: 10.1016/j.plantsci.2018.10.022. Epub 2018 Nov 1.
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 = 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 = 0.91), flowering (F)(h = 0.95), EGF (h = 0.79) and mid grain filling (MGF) (h = 0.71) under the full irrigation treatment, and at booting (B) (h = 0.89), EGF (h = 0.75) in the limited irrigation treatment. UAV-NDVI explained significant variation in grain yield (GY) at EGF (R = 0.86), MGF (R = 0.83) and LGF (R = 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 = 0.40, 0.49 and 0.45) than the limited irrigation treatment (R = 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.
小麦改良计划需要在多个环境中快速评估大量个体地块。与产量和产量相关的生理特性主要相关的植被指数（VI），以及冠层归一化差异植被指数（NDVI）的快速评估，可以协助进行季节选择。使用无人机（UAV）的多光谱图像可以在不同的作物生长阶段轻松评估 VI 特征。在有限灌溉和充分灌溉处理下种植的 32 个小麦品种和品系被调查，使用安装在无人机上的 Sequoia 传感器监测整个生长周期的 NDVI。在处理过程中，从茎伸长（SE）到晚期灌浆（LGF），从 UAV 检测到的 NDVI 与 Greenseeker（GS）之间观察到的相关性范围从 R=0.38 到 0.90。在充分灌溉处理下，在 SE（h=0.91）、开花（F）（h=0.95）、EGF（h=0.79）和中期灌浆（MGF）（h=0.71）期间，UAV-NDVI 也具有较高的遗传力，在有限灌溉处理下，在 B（h=0.89）、EGF（h=0.75）期间，UAV-NDVI 解释了 GY 在灌浆（R=0.86）、MGF（R=0.83）和 LGF（R=0.89）阶段的显著变化，结果与 GS-NDVI 一致。在三个不同的灌浆阶段（R=0.40、0.49 和 0.45），UAV-NDVI 与 GY 之间的相关性高于有限灌溉处理（R=0.08、0.12 和 0.14），在有限灌溉条件下，GY 计算降低了 24.4%。在有限灌溉下灌浆期间，UAV-NDVI 与 GY 之间的 Pearson 相关性也较低，范围从 r=0.29 到 0.37，但高于 GS-NDVI 数据。在与 GY 相关时，归一化差异红边（NDRE）和归一化绿色红差异指数（NGRDI）也表现出类似的模式。在开花后期估算的鲜生物量与 EGF 时的 UAV-NDVI 具有 r=0.30 至 0.51 的显著相关性。一些基因型，如 Nongda 211、Nongda 5181、Zhongmai 175 和 Zhongmai 12，被确定为在灌浆期间使用 NDVI 生成高产量基因型。总之，安装在无人机上的多光谱传感器是一种可靠的高通量 NDVI 测量平台，可用于预测生物量和 GY，并且灌浆阶段似乎是最佳选择期。
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