发表机构
Iowa State University; Translational AI Research and Education Center（爱荷华州立大学; 转化式AI研究与教育中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发了一种低成本的玉米果穗3D形态测量平台，利用消费级设备和NeRF技术实现高效测量，经测试其精度符合要求，可用于育种级高通量表型分析。
AI 中文摘要
玉米果穗的几何形态（长度、宽度、曲率和体积）与产量及灌浆结果密切相关，但现有的高通量表型分析流程仍受限于所需的成本、人力和专用硬件。我们开发并验证了一种低成本流程，该流程可通过在均匀LED照明下，使用消费级数码单反相机（DSLR）在电动转盘上拍摄的单个20秒视频重建玉米果穗的水密3D网格。多视图COLMAP流程得到的相机位姿初始化神经辐射场（NeRF），而每帧都可见的已知直径圆柱支架可提供自动度量缩放，并附带下游几何质量控制。将该流程应用于包含不同玉米自交系的300个果穗，其中250个（83.3%）通过了自动处理和质量控制。在全部250个果穗上，骨架长度与手动卡尺测量结果一致（R²=0.964，RMSE=4.68mm）；在涵盖全尺寸范围的15个果穗子集上，凸包体积与排水法测量体积一致（R²=0.982，RMSE=5.26mL）。残留长度误差随果穗曲率增大而增加，而记录与卡尺相同直线弦长的边界框高度则无此趋势；因此，这种差异源于测量定义，因为卡尺记录弦长，而骨架长度则追踪测地线弧。采集硬件成本约为607美元，每个果穗的操作员参与时间从约5分钟降至1分钟，所有下游处理均无人值守运行。该平台为育种级3D果穗表型分析提供了基础。
英文摘要
Maize ear geometry (length, width, curvature, and volume) is closely tied to yield and grain-filling outcomes, but existing high-throughput phenotyping pipelines remain constrained by the cost, labor, and specialized hardware they require. We developed and validated a low-cost pipeline that reconstructs a watertight 3-D mesh of a maize ear from a single 20-second video captured with a consumer-grade DSLR on a motorized turntable under uniform LED illumination. Camera poses from a multi-seed COLMAP procedure initialize a Neural Radiance Field (NeRF), and a cylindrical holder of known diameter, visible in every frame, provides automatic metric scaling with downstream geometric quality control. Applied to 300 ears spanning a diverse maize inbred panel, 250 (83.3%) passed automated processing and quality control. Skeleton length agreed with manual caliper measurements across all 250 ears (R^2 = 0.964, RMSE = 4.68 mm), and convex-hull volume agreed with water-displacement volume on a 15-ear subset spanning the full size range (R^2 = 0.982, RMSE = 5.26 mL). Residual length error grew with ear curvature, whereas bounding-box height, which records the same straight-line chord as calipers, showed no such trend; the discrepancy therefore originates in the measurement definition, since calipers record the chord while skeleton length traces the geodesic arc. The capture hardware costs approximately 607 USD, and operator involvement fell from roughly five minutes to one minute per ear, with all downstream processing running unattended. The platform provides a foundation for breeding-scale 3-D ear phenotyping.
