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PointNeXt-DBSCAN:一个混合点云深度学习框架,用于多阶段棉花叶实例细分的混合点云

Zeyu Lei^1,2, Debin Zeng^1,3, Liangfang Zheng^1,3

¹College of Information Engineering, Tarim University, Alaer, China.

Frontiers in plant science

|February 16, 2026

概括

这项研究引入了一个新的混合框架,用于从3D点云精确细分棉花叶. 该方法显著提高了识别单个叶子的准确性,有助于自动化植物表型.

科学领域:

农业工程农业工程
计算机视觉计算机视觉
植物科学植物科学

背景情况:

棉花点云中的器官级实例细分由于形态变异和叶子封闭而具有挑战性.
精确的叶子提取对于自动化表型和特征分析至关重要.

研究的目的:

为棉花叶实例细分开发一个高精度的框架.
为了应对细分棉花植物在不同生长阶段的挑战.

主要方法:

提出了一个混合框架,将PointNeXt用于语义细分和密度适应的DBSCAN组合在一起,例如细分.
构建和增强了1065家棉花厂的数据集.
用于语义和实例细分的两个阶段管道被采用.

主要成果:

语义细分实现了0.9846,比PointNet++有7.2%的改进.
实例细分实现了0.983的调整后兰德指数 (ARI),减少了63%的过度细分.
该框架保持了<3%的误差,叶子<5厘米2.

结论:

拟议的混合框架为自动提取棉花关键表型特征提供了可靠的技术支持.
这种方法提高了叶面积指数和叶面倾斜分布测量的准确性.
该框架有效地处理棉花植物的形态变异和叶子封闭.

关键词:

三维点云是3D点云.棉花植物的叶子棉花植物的叶子深度学习是一种深度学习.多个阶段的增长监测.点云细分分点云细分

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