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使用深度学习和集群算法准确地估计田间大豆植物的LAI

Bing Shi¹, Luqi Guo¹, Lejun Yu¹

¹National Key Laboratory for Tropical Crop Breeding, Sanya Research Institute of Hainan University, Hainan University, Sanya, China.

Frontiers in plant science

|February 6, 2025

概括

这项研究引入了一种新的3D点云处理管道,用于高通量大豆表型化. 它使用UAV-LiDAR数据准确地对单个大豆植物进行细分,并估计叶面积指数 (LAI).

科学领域:

农业科学农业科学
遥感遥感遥感遥感
计算机视觉计算机视觉

背景情况:

叶面积指数 (LAI) 对生态系统的生产力至关重要.
传统的表型化方法具有破坏性和劳动密集性.
需要自动化,高通量方法进行植物分析.

研究的目的:

开发和验证3D点云处理管道,用于对野外大豆植物进行细分.
通过UAV-LiDAR数据估计叶面积指数 (LAI).
为了实现高吞吐量大豆表型化.

主要方法:

使用UAV-LiDAR进行3D点云数据采集.
应用PointNet++用于初始的工厂细分和环境噪声消除.
采用流域算法和k-means集群用于单个植物细分.
使用机器学习模型 (SVM,RF,XGBoost) 估计的LAI.

主要成果:

通过PointNet++将细分精度提高了6.73%.
流域算法获得了高F1得分 (0.89-0.90),表现优于k-means.
在SVM模型中,LAI估计的准确度最高 (R2 = 0.79,RMSE = 0.47).

结论:

拟议的管道结合了PointNet++和分水,有效地细分了单个大豆植物.
这种方法为高通量提取植物表型数据提供了基础.
该方法可以快速计算大豆植物形态参数用于表型化.

关键词:

李达尔 (LiDAR) 是一种激光雷达.在 PointNet++++ 中使用.无人机无人机无人机是什么?高通量的高通量机器学习是机器学习.豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆

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