DataSheet_1_Cotton morphological traits tracking through spatiotemporal registration of terrestrial laser scanning time-series data.pdf

Understanding the complex interactions between genotype-environment dynamics is fundamental for optimizing crop improvement. However, traditional phenotyping methods limit assessments to the end of the growing season, restricting continuous crop monitoring. To address this limitation, we developed a methodology for spatiotemporal registration of time-series 3D point cloud data, enabling field phenotyping over time for accurate crop growth tracking. Leveraging multi-scan terrestrial laser scanning (TLS), we captured high-resolution 3D LiDAR data in a cotton breeding field across various stages of the growing season to generate four-dimensional (4D) crop models, seamlessly integrating spatial and temporal dimensions. Our registration procedure involved an initial pairwise terrain-based matching for rough alignment, followed by a bird’s-eye view adjustment for fine registration. Point clouds collected throughout nine sessions across the growing season were successfully registered both spatially and temporally, with average registration errors of approximately 3 cm. We used the generated 4D models to monitor canopy height (CH) and volume (CV) for eleven cotton genotypes over two months. The consistent height reference established via our spatiotemporal registration process enabled precise estimations of CH (R2 = 0.95, RMSE = 7.6 cm). Additionally, we analyzed the relationship between CV and the interception of photosynthetically active radiation (IPARf), finding that it followed a curve with exponential saturation, consistent with theoretical models, with a standard error of regression (SER) of 11%. In addition, we compared mathematical models from the Richards family of sigmoid curves for crop growth modeling, finding that the logistic model effectively captured CH and CV evolution, aiding in identifying significant genotype differences. Our novel TLS-based digital phenotyping methodology enhances precision and efficiency in field phenotyping over time, advancing plant phenomics and empowering efficient decision-making for crop improvement efforts.

解析基因型-环境动态间的复杂互作关系，是优化作物改良工作的核心基础。然而传统表型分析方法仅能在作物生育期结束时开展评估，极大限制了作物的连续监测工作。为解决这一局限，我们开发了一套时序三维点云数据的时空配准方法，可实现长期田间表型分析以精准追踪作物生长动态。我们依托多扫描地面激光扫描（TLS）技术，在棉花育种田的全生育期多个阶段采集高分辨率三维激光雷达（LiDAR）数据，构建四维（4D）作物模型，实现空间与时间维度的无缝整合。本次配准流程首先采用基于地形的两两匹配完成粗配准，随后通过鸟瞰视角调整实现精配准。我们在整个生育期内共开展9次数据采集，所获点云数据均实现了有效的时空配准，平均配准误差约为3厘米。利用生成的4D模型，我们对11个棉花基因型在两个月内的冠层高度（CH）与冠层体积（CV）进行了动态监测。通过本次时空配准流程建立的稳定高度参考基准，实现了冠层高度的精准估算（决定系数R²=0.95，均方根误差RMSE=7.6厘米）。此外，我们分析了冠层体积与光合有效辐射截获量（IPARf）之间的关联，发现二者遵循与理论模型一致的指数饱和曲线，回归标准误差（SER）为11%。我们还对比了用于作物生长建模的理查兹家族S型曲线类数学模型，发现逻辑斯蒂模型可有效捕捉冠层高度与冠层体积的动态演化过程，助力识别不同棉花基因型间的显著差异。本研究提出的新型基于TLS的数字化表型分析方法，提升了长期田间表型分析的精度与效率，推动了植物表型组学研究发展，可为作物改良工作提供高效的决策支持。