High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing
High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing
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DOI：
10.3390/rs8121031
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发表时间：
2016-12-01
期刊：
影响因子：
5
通讯作者：
Hawkesford, Malcolm J.
中科院分区：
文献类型：
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作者：
Holman, Fenner H.;Riche, Andrew B.;Hawkesford, Malcolm J.
人们越来越需要提高全球作物产量，同时尽量减少土地、肥料和水等资源的使用。农业研究人员利用地面观测来鉴定、选择和培育具有有利基因型和表型的作物；然而，在开放领域收集快速、高质量和高容量表型数据的能力限制了这一点。本研究开发并评估了一种方法，该方法通过运动结构（SfM）摄影测量技术生成的作物田间试验的三维数字表面模型，从多时相、非常高的空间分辨率（1厘米/像素）中快速获取作物高度和生长速度，该方法使用的是安装有红绿蓝（RGB）相机的无人驾驶飞行器（UAV）通过反复飞行收集的航空图像。我们将无人机SfM模拟的作物高度与地面激光扫描仪（TLS）得出的作物高度以及使用2米规则进行的标准田间作物高度测量进行了比较。与现有的手动2米规则方法相比，最精确的无人机衍生表面模型和TLS都实现了0.03米的均方根误差（RMSE）。然后将优化后的无人机方法应用于一个冬小麦大田的生长季节表型试验，该试验包含25个不同品种，种植在27 m(2)块中，并进行四种不同的氮肥处理。作物生长不同阶段的精度评估结果均显示RMSE值较低（5月、6月和7月的RMSE值分别为0.07、0.02和0.03 m），这使得作物生长速率可以通过多时段地表模式的差异得到。我们发现生长率从-13毫米/天到17毫米/天不等。我们的研究结果清楚地显示了不同氮肥用量对作物生长的影响。所产生的数字地表模型提供了一种新的作物高度变化的空间映射，无论是在田间尺度上还是在单个地块内。该研究证明基于无人机的SfM有潜力成为田间作物高度高通量表型的新标准。
There is a growing need to increase global crop yields, whilst minimising use of resources such as land, fertilisers and water. Agricultural researchers use ground-based observations to identify, select and develop crops with favourable genotypes and phenotypes; however, the ability to collect rapid, high quality and high volume phenotypic data in open fields is restricting this. This study develops and assesses a method for deriving crop height and growth rate rapidly from multi-temporal, very high spatial resolution (1 cm/pixel), 3D digital surface models of crop field trials produced via Structure from Motion (SfM) photogrammetry using aerial imagery collected through repeated campaigns flying an Unmanned Aerial Vehicle (UAV) with a mounted Red Green Blue (RGB) camera. We compare UAV SfM modelled crop heights to those derived from terrestrial laser scanner (TLS) and to the standard field measurement of crop height conducted using a 2 m rule. The most accurate UAV-derived surface model and the TLS both achieve a Root Mean Squared Error (RMSE) of 0.03 m compared to the existing manual 2 m rule method. The optimised UAV method was then applied to the growing season of a winter wheat field phenotyping experiment containing 25 different varieties grown in 27 m(2) plots and subject to four different nitrogen fertiliser treatments. Accuracy assessments at different stages of crop growth produced consistently low RMSE values (0.07, 0.02 and 0.03 m for May, June and July, respectively), enabling crop growth rate to be derived from differencing of the multi-temporal surface models. We find growth rates range from -13 mm/day to 17 mm/day. Our results clearly display the impact of variable nitrogen fertiliser rates on crop growth. Digital surface models produced provide a novel spatial mapping of crop height variation both at the field scale and also within individual plots. This study proves UAV based SfM has the potential to become a new standard for high-throughput phenotyping of in-field crop heights.