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基于3D点云的中国Cymbidium幼苗的自动化表型化方法

Yang Zhou^1,2, Honghao Zhou³, Yue Chen⁴

¹College of Electronic and Information Engineering, Zhejiang university of science and technology, HangZhou, 310023, ZheJiang, China. zybuaa@163.com.

|September 29, 2024

概括

本研究引入了一种自动化方法,用于使用点云数据测量Cymbidium幼苗的表型参数. 新的细分算法准确地捕捉了结构,提高了植物高度,叶数和叶面积的测量准确度.

科学领域:

植物科学植物科学
计算机视觉计算机视觉
农业技术农业技术

背景情况:

手动确定Cymbidium幼苗的表型参数是低效和昂贵的.
准确的表型数据对于植物育种和研究至关重要.

研究的目的:

开发一个完全自动化的测量方案,用于Cymbidium苗木的表型参数.
为了应对基于其形态学的个别耕种者的细分挑战.

主要方法:

设计了一种新的点云细分方法,涉及两轮分离重叠的部件.
细分是基于边缘点云和水平切片,由结构加权.
该算法与子生长方向对齐,以准确地提取骨架点.

主要成果:

自动化系统准确计算了五个表型参数:植物高度,叶数,叶长,叶宽和叶面积.
对于各自的参数,获得的准确率为98.6%,100%,92.2%,89.1%和82.3%.
与人工方法相比,该方法显著提高了表型参数的预测准确性.

结论:

拟议的自动化方案为测量Cymbidium幼苗表型提供了一种高效且具有成本效益的解决方案.
细分算法的准确性在捕捉形态方面确保可靠的表型数据用于研究和应用.

关键词:

3D点云数据数据 3D点云数据中国的比.现型化 (Phenotyping) 是一种表现方式.分段化分段化分段化分段化

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