基于三维重建的玉米果穗表型自动化分析
Automated Maize Ear Phenotyping Using 3D Reconstructions
- Iowa State University（爱荷华州立大学）
- Translational AI Research and Education Center（转化人工智能研究与教育中心）
- Fayetteville State University（费耶特维尔州立大学）
- Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
该研究开发了基于COLMAP、NeRF、Cellpose-SAM等工具的自动化流程，从玉米果穗三维点云提取性状，在测试集上取得良好精度，生成的数据集可用于表型-基因型关联分析。
中文摘要 AI 辅助
受遗传因素影响，玉米穗行数、每行粒数、粒大小等籽粒性状存在较大差异，且这些性状与影响产量的基因组区域密切相关。然而，人工测量这些性状的速度无法跟上育种项目产生的大量玉米样本。为解决该问题，我们开发并验证了一套完全自动化的流程，用于从玉米果穗的三维点云中提取上述性状，该流程基于最新的视频转点云平台构建。原始视频帧经COLMAP和NeRF处理后，通过基于密度的分离方法提取果穗，再将点云校准至物理单位。校准后的果穗点云通过PCA进行Z轴对齐，并以圆柱展开方式转换为二维图像。我们增强了图像对比度，并使用Cellpose-SAM进行零微调实例分割，采用三重并置展开策略以防止接缝处的重复计数。在包含268个标注果穗的数据集中，168个留作测试的果穗上，该流程实现了籽粒计数的决定系数R²=0.921（平均绝对百分比误差MAPE=10.33%），95.2%的果穗穗行数误差在±2行以内（平均绝对误差MAE=0.75行）。由此得到的多性状数据集包含每个果穗的已知基因型，可用于表型-基因型关联分析。
英文摘要
Maize kernel traits such as row number, kernels per row, and kernel size vary largely for genetic reasons and are consistently associated with regions of the genome that influence yield. Manual measurement of these traits, however, cannot keep pace with the volume of maize generated in a breeding program. To address this, we developed and validated a fully automated pipeline for extracting these traits from 3D point clouds of corn ears, built on a recently developed video-to-point-cloud platform. Raw video frames are processed through COLMAP and NeRF, the ear is isolated via density-based separation, and the point cloud is distance-calibrated to physical units. The calibrated ear point cloud was Z-axis aligned via PCA and cylindrically unwrapped to a 2D image. We enhanced contrast and performed zero-fine-tuning instance segmentation using Cellpose-SAM. A triple-juxtaposed unwrap strategy was used to prevent double-counting at the seam. The pipeline achieved kernel count R^2 = 0.921 (MAPE = 10.33%) and kernel row number within +-2 rows for 95.2% of ears (MAE = 0.75 rows) on a 168-ear held-out set from the 268-ear labeled dataset. The resulting multi-trait dataset has known genotype identity for each ear, positioning it for phenotype-to-genotype association analyses.
