Hämmerle Martin, Höfle Bernhard
GIScience Research Group, Institute of Geography, Heidelberg University, Im Neuenheimer Feld 368, 69120 Heidelberg, Germany.
GIScience Research Group, Institute of Geography, Heidelberg University, Im Neuenheimer Feld 368, 69120 Heidelberg, Germany ; Heidelberg Center for the Environment (HCE), Heidelberg University, 69120 Heidelberg, Germany.
Plant Methods. 2016 Nov 28;12:50. doi: 10.1186/s13007-016-0150-6. eCollection 2016.
In agriculture, information about the spatial distribution of crop height is valuable for applications such as biomass and yield estimation, or increasing field work efficiency in terms of fertilizing, applying pesticides, irrigation, etc. Established methods for capturing crop height often comprise restrictions in terms of cost and time efficiency, flexibility, and temporal and spatial resolution of measurements. Furthermore, crop height is mostly derived from a measurement of the bare terrain prior to plant growth and measurements of the crop surface when plants are growing, resulting in the need of multiple field campaigns. In our study, we examine a method to derive crop heights directly from data of a plot of full grown maize plants captured in a single field campaign. We assess continuous raster crop height models (CHMs) and individual plant heights derived from data collected with the low-cost 3D camera Microsoft Kinect for Xbox One™ based on a comprehensive comparison to terrestrial laser scanning (TLS) reference data.
We examine single measurements captured with the 3D camera and a combination of the single measurements, i.e. a combination of multiple perspectives. The quality of both CHMs, and individual plant heights is improved by combining the measurements. R of CHMs derived from single measurements range from 0.48 to 0.88, combining all measurements leads to an R of 0.89. In case of individual plant heights, an R of 0.98 is achieved for the combined measures (with R = 0.44 for the single measurements). The crop heights derived from the 3D camera measurements comprise an average underestimation of 0.06 m compared to TLS reference values.
We recommend the combination of multiple low-cost 3D camera measurements, removal of measurement artefacts, and the inclusion of correction functions to improve the quality of crop height measurements. Operating low-cost 3D cameras under field conditions on agricultural machines or on autonomous platforms can offer time and cost efficient tools for capturing the spatial distribution of crop heights directly in the field and subsequently to advance agricultural efficiency and productivity. More general, all processes which include the 3D geometry of natural objects can profit from low-cost methods producing 3D geodata.
在农业中，作物高度的空间分布信息对于生物量和产量估算等应用，或在施肥、施药、灌溉等方面提高田间作业效率具有重要价值。已有的获取作物高度的方法在成本、时间效率、灵活性以及测量的时间和空间分辨率方面往往存在限制。此外，作物高度大多源自植物生长前裸地的测量以及植物生长时作物表面的测量，这就需要进行多次田间作业。在我们的研究中，我们探讨了一种直接从单次田间作业中获取的全生长玉米植株地块数据推导作物高度的方法。我们基于与地面激光扫描（TLS）参考数据的全面比较，评估了连续栅格作物高度模型（CHM）以及从基于低成本的适用于Xbox One™的3D相机Microsoft Kinect收集的数据中得出的单株植物高度。
我们研究了用3D相机获取的单次测量数据以及单次测量数据的组合，即多个视角的组合。通过组合测量数据，CHM和单株植物高度的质量都得到了提高。单次测量得出的CHM的R值范围为0.48至0.88，组合所有测量数据后R值达到0.89。对于单株植物高度，组合测量的R值为0.98（单次测量的R值为0.44）。与TLS参考值相比，从3D相机测量得出的作物高度平均低估了0.06米。
我们建议组合多次低成本3D相机测量数据，去除测量伪像，并纳入校正函数以提高作物高度测量的质量。在田间条件下，在农业机械或自主平台上操作低成本3D相机可为直接在田间获取作物高度的空间分布提供省时且经济高效的工具，进而提高农业效率和生产力。更普遍地说，所有涉及自然物体3D几何形状的过程都可以从生成3D地理数据的低成本方法中受益。
