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基于PointNeXt和Quickshift+的自动3D植物器官实例细分方法

Sifan Dong¹, Xueyan Fan¹, Xiuhua Li^1,2

¹State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, School of Electrical Engineering, Guangxi University, Nanning 530004, China.

Plant phenomics (Washington, D.C.)

|December 19, 2025

概括

使用PointNeXt和Quickshift++的新两阶段方法实现了对各种植物类型的精确器官实例细分,推进了植物表型研究.

科学领域:

计算机视觉计算机视觉
植物科学植物科学
农业技术农业技术

背景情况:

对3D植物点云的精确器官实例细分对于植物表型化至关重要.
现有的方法往往缺乏跨不同作物类型 (单形与双形) 的概括性.

研究的目的:

为单一植物器官实例细分开发一个通用的两阶段方法.
为了提高植物器官在不同物种的细分的准确性和适用性.

主要方法:

一种两阶段的方法,结合了改进的PointNeXt用于语义细分 (茎,叶) 和Quickshift++用于例如细分.
培训和验证各种数据集,包括甘,玉米和西红点云.

主要成果:

实现了高的语义细分精度 (最多96.96%,最多87.15%).
在实例细分方面表现优于最先进的方法 (mPrec 93.32%,mRec 85.60%,mF1 87.94%,mIoU 81.46%).
在不同植物物种和早期生长阶段表现出强烈的泛化.

结论:

拟议的方法为3D植物点云中的器官实例细分提供了更高的概括性.
这种方法提供了一个强大的工具,可以在各种作物中推进植物表型研究.

关键词:

器官细分器官细分器官细分植物表型定型植物表型定型一个点云点云.在 PointNeXtt 的位置上.快速变速器++++ 快速变速器

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