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Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle

机译：用无人空中车辆对小麦农学及育种试验的NDVI动态监测

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While new technologies can capture high-resolution normalized difference vegetation index (NDVI), a surrogate for biomass and leaf greenness, it is a challenge to efficiently apply this technology in a large breeding program. Here we validate a high-throughput phenotyping platform to dynamically monitor NDVI during the growing season for the contrasting wheat cultivars and managements. The images were rapidly captured (approximately 1 ha in 10 min) by an unmanned aerial vehicle (UAV) carrying a multi-spectral camera (RedEdge) at low altitude (30-50 m, 2-5 cm(2) pixel size). NDVIs for individual plots were extracted from the reflectance at Red and Near Infrared wavelengths represented in a reconstructed and segmented ortho-mosaic. NDVI measured by UAV and RedEdge camera were strongly correlated with those measured by hand held GreenSeeker (R-2 = 0.85) but were offset with UAV readings about 0.2 units higher and more compressed. The high-throughput phenotyping platform captured the variation of NDVI among cultivars and treatments (i.e. irrigation, nitrogen and sowing). During the growing season, the NDVI approached saturation around flowering time (similar to 0.92), then gradually decreased until maturity (similar to 0.35). Strong correlations were found between image NDVI around flowering time and final yield (R-2 = 0.82). Given that the image NDVI includes signals from background (soil and senescenced leaves), ground cover from a high resolution hand-held camera was used to adjust the NDVI from UAV. This slightly increased the correlation between adjusted NDVI and yield (R-2 = 0.87). The high-throughput phenotyping platform in this study can be used in agronomy, physiology and breeding to explore the complex interaction of genotype, environment and management. Data fusion from ground and aerial sampling improved the accuracy of low resolution data to integrate observations across multiple scales.
机译：虽然新技术可以捕获高分辨率归一化差异植被指数（NDVI），但替代生物质和叶片绿色，虽然有效地应用了大量育种计划是一项挑战。在这里，我们验证了一个高吞吐量的表型平台，在生长季节期间动态监测NDVI，以为对比小麦品种和管理。通过在低空（30-50μm，2-5cm（2）像素尺寸）上携带多光谱相机（URDEDGE）的无人的空中车辆（UAV），通过无人驾驶的空中车辆（UAV）迅速捕获（大约1公顷）。单个图的NDVI是从重建和分段的邻摩尔马赛克中表示的红色和近红外波长的反射率提取。由UAV和REDEDGE CAMERS测量的NDVI与手工持有的GreenSeeker（R-2 = 0.85）测量的那些强烈相关（R-2 = 0.85），但与UAV读数较高约0.2单位更高，更压缩。高通量表型平台捕获了品种和治疗中NDVI的变异（即灌溉，氮和播种）。在生长季节期间，NDVI在开花时间附近饱和（类似于0.92），然后逐渐降低直至成熟（类似于0.35）。在开花时间和最终产量周围的图像NDVI之间发现了强相关性（R-2 = 0.82）。鉴于图像NDVI包括来自背景（土壤和中去的叶子）的信号，来自高分辨率手持相机的地面盖子用于调节UAV的NDVI。这种略微增加了调整的NDVI和产量之间的相关性（R-2 = 0.87）。本研究中的高通量表型平台可用于农艺，生理学和育种，以探讨基因型，环境和管理的复杂相互作用。地面和空中采样的数据融合提高了低分辨率数据的准确性，以跨多个尺度集成观测。

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