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基于NeRF的3D重建管道,用于获取和分析番茄作物形态

Hong-Beom Choi¹, Jae-Kun Park¹, Soo Hyun Park¹

¹Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung, Gangwon, Republic of Korea.

Frontiers in plant science

|November 8, 2024

概括

这项研究引入了一个新的3D作物表型系统,使用神经辐射场 (NeRF) 进行精确的,非破坏性的测量. 该技术准确地分析植物特征,如温室中的叶面积和果实体积.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
植物生物学植物生物学

背景情况:

传统的二维作物成像方法在捕捉详细的植物形态方面存在局限性.
数字表型分析为农作物中的遗传特征表达提供了先进的见解.

研究的目的:

介绍使用神经辐射场 (NeRF) 技术的新型3D表型管道.
为了能够在温室环境中准确,非破坏性地测量作物参数.

主要方法:

使用无人温室机器人进行自动RGB图像采集.
应用NeRF技术用于密集的点云生成.
非破坏性测量作物参数,如节点长度,叶面积和果实体积.

主要成果:

3D表型化结果与传统的人体成长调查之间存在很高的相关性.
准确测量节点间长度 (R-平方:0.973,MAPE:0.089) 的方法.
精确地测量了叶面积 (R-平方:0.953,MAPE:0.090) 和果子体积 (R-平方:0.96,MAPE:0.135).

结论:

基于NeRF的3D表型化管道是温室农业的实用和精确工具.
该系统实现了详细的形态分析,即使在有限的摄像头视角.
这项技术对推进现代农业实践具有重大前景.

关键词:

3D表型定制 3D表型定制自动增长测量自动增长测量温室应用程序温室应用程序神经辐射场是一个神经辐射场.一个点云,一个点云.

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