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Yangyang Gu¹, Hongxu Ai¹, Tai Guo¹

¹National Engineering and Technology Center for Information Agriculture (NETCIA), Zhongshan Biological Breeding Laboratory (ZSBBL), MARA Key Laboratory for Crop System Analysis and Decision Making, MOE Engineering Research Center of Smart Agriculture, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, One Weigang, Nanjing, Jiangsu, 210095, People's Republic of China.

|November 25, 2023

概括

这项研究引入了两个新的算法,DBSC和VBRG,用于使用地面激光扫描准确估计小麦数量. DBSC算法表现出卓越的性能,为作物产量评估提供了一种更有效,更客观的方法.

科学领域:

农业科学农业科学
遥感技术遥感技术遥感技术
计算几何学的计算几何学

背景情况:

准确的小麦产量评估依赖于诸如每单位面积的数等指标,传统上是手动计数的.
手动计数是低效的,劳动密集的,不准确的,缺乏客观性.
需要新的,自动化的方法来精确估计小麦数量.

研究的目的:

开发和评估使用地面激光扫描 (TLS) 估计小麦耳数量的两个新算法.
为了比较基于密度的空间聚类 (DBSC) 和基于voxel的区域增长 (VBRG) 算法的性能.
为小麦产量表型提供高效,非破坏性的方法.

主要方法:

开发了两个算法:DBSC和VBRG,利用TLS点云数据.
DBSC涉及基于正常向量差异和基于密度的聚类的点云细分.
VBRG涉及声化,使用k-d树构建拓关系,以及用于耳朵检测的区域增长.

主要成果:

无论是DBSC还是VBRG,在估计各种条件下的小麦数量方面都表现出有希望 (RMSE 76114/m2,rRMSE 18.6227.96%,r 0.760.84).
DBSC (RMSE 76耳朵/m2,rRMSE 18.62%,r 0.84) 的表现优于VBRG (RMSE 114耳朵/m2,rRMSE 27.96%,r 0.76) 的表现.
通过使用点作为单位,DBSC保留了更详细的信息,使其更适合基于田间的小麦数估计.

结论:

开发的DBSC和VBRG算法为非破坏性小麦数量测量提供了创新的方法.
这些方法提高了小麦产量表型评估的效率和客观性.
DBSC算法对于详细准确地估计小麦数量特别有效.

关键词:

基于密度的空间聚类基于正常 (DBSC).耳朵号码耳朵号码李达尔 (LiDAR) 是一种激光雷达.基于voxel的区域增长 (VBRG)

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相关概念视频

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.