Recent developments in remote sensing are enabling automatic, high resolution, and non-destructive survey of agriculture fields, providing the key basis for advancing plant breeding. Among the used remote sensing modalities, LiDAR has attracted wide attention for its ability to directly provide accurate 3D information. Despite the increasing utilization of LiDAR technology in phenotyping, there is still a lack of effective quality control strategies, in particular, quality control of LiDAR data collected on a multi-temporal basis. This study proposes a targetless framework for multi-temporal LiDAR data quality control and crop characterization in mechanized agricultural fields. Features extracted from the fields – terrain patches and row/alley locations – are utilized for evaluating the vertical and planimetric relative accuracy of the point clouds. Row/alley locations in the field are automatically identified from the point clouds based on the assumption that higher point density and/or higher elevation correspond to plant locations. The performance of the proposed quality control strategies is evaluated using multi-temporal datasets collected in agricultural fields of different sizes, orientation, crops, and growth stages. The result shows that the net vertical and planimetric discrepancies between multi-temporal point clouds are ±3 cm and ±8 cm, respectively. While the former reflects the actual accuracy of the point clouds, the latter is a combined effect of the LiDAR point cloud accuracy, rasterization artifacts, crop type, growth pattern, and wind condition during data acquisition. In terms of row and alley detection, the result shows that the proposed strategy achieves high performance and can deal with different planting orientation, crop types, growth stages, canopy cover, and planting density. In conclusion, this study presents a quality control framework for multi-temporal LiDAR data. Moreover, the row and alley detection leads to automated extraction of plots, and hence facilitates the use of remotely sensed data for automated phenotyping.

遥感技术的最新发展正在实现对农业领域的自动，高分辨率和无损调查，这为推进植物育种提供了关键基础。在使用的遥感方式中，LiDAR直接提供准确的3D信息的能力引起了广泛的关注。尽管在表型分析中越来越多地使用LiDAR技术，但仍然缺乏有效的质量控制策略，尤其是在多时间基础上收集的LiDAR数据的质量控制。本研究为机械化农业领域的多时相LiDAR数据质量控制和作物表征提出了一种无目标的框架。从野外提取的特征（地形斑块和行/巷位置）可用于评估点云的垂直和平面相对精度。根据更高的点密度和/或更高的海拔高度对应于工厂位置的假设，从点云中自动识别田野中的行/巷位置。使用从不同大小，方向，作物和生长阶段的农业领域中收集的多时间数据集来评估所提出的质量控制策略的性能。结果表明，多时相点云之间的净垂直和平面差异分别为±3 cm和±8 cm。前者反映了点云的实际精度，而后者则是LiDAR点云精度，栅格化伪像，数据采集​​期间的作物类型，生长方式和风况。在行和胡同检测方面，结果表明，该策略具有较高的性能，可以处理不同的种植方向，作物类型，生长阶段，冠层覆盖和种植密度。总之，本研究提出了多时相LiDAR数据的质量控制框架。而且，行和胡同的检测导致了图的自动提取，因此有利于将遥感数据用于自动表型化。这项研究提出了多时相LiDAR数据的质量控制框架。而且，行和胡同的检测导致了图的自动提取，因此有利于将遥感数据用于自动表型化。这项研究提出了多时相LiDAR数据的质量控制框架。而且，行和胡同的检测导致了图的自动提取，因此有利于将遥感数据用于自动表型化。