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巴尼克尔NeRF:低成本,高精度的在现场表型的米饭饼用智能手机

Xin Yang^1,2, Xuqi Lu^1,2, Pengyao Xie^1,2

¹College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.

Plant phenomics (Washington, D.C.)

|December 6, 2024

概括

研究人员开发了PanicleNeRF,这是一种基于智能手机的新方法,用于在现场精确的3D大米饼重建. 这种低成本的表型化工具准确地提取特征,有助于加速米育种.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
遗传学遗传学是一个

背景情况:

米的特征对谷物产量至关重要,也是表型的关键焦点.
现有的表型化方法通常需要控制的室内设置,限制了现场应用.
准确地在田间测量米的特征对于育种计划至关重要.

研究的目的:

开发一种新的,低成本的,高精度的方法,以在自然现场条件下进行3D大米的重建.
通过使用智能手机获取的视频,实现大米饼的高通量表型化.
为了促进改善的品种的加速育种.

主要方法:

开发了PanicleNeRF,集成了Segment Anything Model (SAM) 和YOLOv8,用于精确的二维图像分割.
利用神经辐射场 (NeRF) 来从细分图像中重建3D模型.
处理3D点云来提取关键的恐慌特征并评估它们与产量组件的相关性.

主要成果:

与YOLOv8.8相比,PanicleNeRF实现了高细分精度 (平均F1得分为86.9%,IOU为79.8%) 和优越的边界重叠.
3D重建质量明显超过了传统的结构-从-运动多视图立体声 (SfM-MVS) 方法.
精确提取长度 (rRMSE < 3%) 和估计体积和粒数 (R2 > 0.82) 和质量 (R2 > 0.76) 之间的强相关性.

结论:

PanicleNeRF提供了一种有效的,低成本的解决方案,用于现场米面的表型.
该方法可实现高通量数据采集,加速米育种效率.
这种方法克服了室内表型的局限性,提供了有价值的现场数据.

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