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使用点云的3D田间作物表型的进展:对传感器技术,目标特征和在受控和现场条件下的挑战进行比较的审查

Emmanuel Omia¹, Eunsung Park², Dennis Semyalo²

¹Department of Smart Agriculture Systems Machinery Engineering, Chungnam National University, Daejeon, Republic of Korea.

Frontiers in plant science

|February 23, 2026

概括

在3D作物表型的进步利用点云技术进行详细的植物分析. 本研究回顾了在受控和现场环境中的传感器应用,强调了农业技术的挑战和未来方向.

科学领域:

农业科学农业科学
植物生物学植物生物学
计算机视觉计算机视觉

背景情况:

3D表型定型使用3D成像量化植物结构和形态.
高通量,非破坏性3D成像技术精确地测量植物特征.
该领域已经从受控环境演变为现实世界的实地应用,用于大规模作物分析.

研究的目的:

通过使用点云技术,回顾最近在3D作物表型学方面的进展.
为了比较传感器技术及其在受控 (室内作物表型,CCP) 和现场 (现场作物表型,FCP) 设置中的应用.
确定农业3D表型化研究的研究缺口和未来方向.

主要方法:

利用了多视频立体声 (MVS) 重建,LiDAR和激光三角测量用于3D数据采集.
比较传感器平台,包括地面机器人和无人机 (UAV).
分析了FCP中的挑战,例如封闭,风和光变性,与受控的CCP环境相比.

主要成果:

3D表型技术能够高通量,非破坏性测量诸如树冠结构和叶面积等特征.
中央共产党为精确的特征测量提供了受控的条件,而FCP面临着环境复杂性.
无人机和机器人系统显示出克服FCP挑战的潜力,例如封闭和传感器范围限制.

结论:

将3D表型技术从实验室转移到现场对于农业应用至关重要.
从3D数据中精确的表型特征提取可以显著改善作物管理和育种.
未来的研究应该专注于提高现场应用的3D表型化的可扩展性和稳定性.

关键词:

3D作物表型化 3D作物表型化激光三角测量是一种激光三角测量.植物现象学植物现象学精准农业精准农业精准农业立体视觉视觉的立体视觉结构光的结构光.地球上的激光.

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