Duan Tao, Zheng Bangyou, Guo Wei, Ninomiya Seishi, Guo Yan, Chapman Scott C
CSIRO Agriculture and Food, Queensland Biosciences Precinct, 306 Carmody Road, St Lucia, Qld 4067, Australia.
Institute for Sustainable Agro-ecosystem Services, The University of Tokyo, Tokyo 188-0002, Japan.
Funct Plant Biol. 2016 Feb;44(1):169-183. doi: 10.1071/FP16123.
Ground cover is an important physiological trait affecting crop radiation capture, water-use efficiency and grain yield. It is challenging to efficiently measure ground cover with reasonable precision for large numbers of plots, especially in tall crop species. Here we combined two image-based methods to estimate plot-level ground cover for three species, from either an ortho-mosaic or undistorted (i.e. corrected for lens and camera effects) images captured by cameras using a low-altitude unmanned aerial vehicle (UAV). Reconstructed point clouds and ortho-mosaics for the whole field were created and a customised image processing workflow was developed to (1) segment the 'whole-field' datasets into individual plots, and (2) 'reverse-calculate' each plot from each undistorted image. Ground cover for individual plots was calculated by an efficient vegetation segmentation algorithm. For 79% of plots, estimated ground cover was greater from the ortho-mosaic than from images, particularly when plants were small, or when older/taller in large plots. While there was a good agreement between the ground cover estimates from ortho-mosaic and images when the target plot was positioned at a near-nadir view near the centre of image (cotton: R2=0.97, sorghum: R2=0.98, sugarcane: R2=0.84), ortho-mosaic estimates were 5% greater than estimates from these near-nadir images. Because each plot appeared in multiple images, there were multiple estimates of the ground cover, some of which should be excluded, e.g. when the plot is near edge within an image. Considering only the images with near-nadir view, the reverse calculation provides a more precise estimate of ground cover compared with the ortho-mosaic. The methodology is suitable for high throughput phenotyping for applications in agronomy, physiology and breeding for different crop species and can be extended to provide pixel-level data from other types of cameras including thermal and multi-spectral models.
地面覆盖是影响作物辐射捕获、水分利用效率和谷物产量的重要生理特性。对于大量地块，尤其是高大作物品种，要以合理的精度高效测量地面覆盖具有挑战性。在这里，我们结合了两种基于图像的方法，从使用低空无人机（UAV）拍摄的正射镶嵌图或未失真（即校正了镜头和相机效果）的图像中估计三种作物品种的地块级地面覆盖。创建了整个田地的重建点云正射镶嵌图，并开发了定制的图像处理工作流程，以（1）将“全场”数据集分割为单个地块，以及（2）从每个未失真图像“反向计算”每个地块。通过高效的植被分割算法计算单个地块的地面覆盖。对于79%的地块，正射镶嵌图估计的地面覆盖大于图像估计值，特别是当植物较小时，或在大地块中植物较老/较高时。当目标地块位于图像中心附近的近天底视图时，正射镶嵌图和图像的地面覆盖估计之间有很好的一致性（棉花：R2 = 0.97，高粱：R2 = 0.98，甘蔗：R2 = 0.84），但正射镶嵌图估计值比这些近天底图像的估计值大5%。由于每个地块出现在多个图像中，因此有多个地面覆盖估计值，其中一些应该排除，例如当地块靠近图像边缘时。仅考虑近天底视图的图像，与正射镶嵌图相比，反向计算提供了更精确的地面覆盖估计。该方法适用于不同作物品种在农学、生理学和育种中的高通量表型分析，并且可以扩展以提供来自包括热成像和多光谱模型在内的其他类型相机的像素级数据。
