刘刚,张伟洁,郭彩玲.基于动态K阈值的苹果叶片点云聚类与生长参数提取[J].农业机械学报,2019,50(4):163-169,178.
LIU Gang,ZHANG Weijie,GUO Cailing.Apple Leaf Point Cloud Clustering Based on Dynamic-K-threshold and Growth Parameters Extraction[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(4):163-169,178.
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基于动态K阈值的苹果叶片点云聚类与生长参数提取   [下载全文]
Apple Leaf Point Cloud Clustering Based on Dynamic-K-threshold and Growth Parameters Extraction   [Download Pdf][in English]
投稿时间:2018-10-10  
DOI:10.6041/j.issn.1000-1298.2019.04.018
中文关键词:  苹果树  三维点云  生长参数  动态K阈值  LCCP算法  K-means算法
基金项目:国家重点研发计划项目(2017YFD0700503)和河北省高等学校科学技术研究项目(QN2017417)
作者单位
刘刚 中国农业大学 
张伟洁 中国农业大学 
郭彩玲 中国农业大学
唐山学院 
中文摘要:根据冠层点云的分布特征,提出一种基于动态K阈值的叶片点云聚类及生长参数提取方法。首先,采用地面三维激光扫描仪获取多站点云数据并完成配准、去噪和抽稀等预处理;然后,随机截取整株点云中的一枝作为研究对象,融合局部凹凸性算法(LCCP)并改进K-means算法,提出基于动态K阈值的叶片点云聚类方法;最后,采用主成分分析方法(PCA)计算叶片点云法平面方向向量,并根据叶片边界点与中心点的位置关系,计算叶宽、叶长等生长参数。试验结果表明,与传统的点云聚类方法相比,本文方法能够在不损失枝干点云的前提下,精确地分割单叶片,保证了聚类结果的完整性和彻底性;与传统的降维方法相比,本文基于真实三维空间信息提取叶片生长参数能够较大程度提高提取准确性,为进一步评价果树冠层光照分布及果园智能化管理提供技术支持。
LIU Gang  ZHANG Weijie  GUO Cailing
China Agricultural University,China Agricultural University and China Agricultural University;Tangshan University
Key Words:apple tree  3D point cloud  growth parameters  dynamic-K-threshold  LCCP algorithm  K-means algorithm
Abstract:Study on the three-dimensional (3D) canopy reconstruction of fruit trees plays a fundamental role in the calculation of canopy illumination distribution and the realization of automatic pruning. According to the characteristics of the leafy tree point cloud, an apple tree leaf points clustering method based on dynamic-K-threshold and a growth parameters extraction method were proposed. Firstly, terrestrial laser scanner Trimble TX8 was chosen to obtain dense point cloud of apple tree canopy from different viewpoints, and then multi station point cloud registration, outlier removal and point cloud simplification were accomplished, so as to reduce the influence of discrete points on calculation results of spatial characteristic parameters. Secondly, intercepting one branch randomly, synthesizing LCCP algorithm and improved K-means algorithm to construct the leaf points clustering method based on dynamic-K-threshold. Thirdly, as the input data, the leaf point cloud was used to construct the covariance matrix based on the PCA to calculate the fitting plane normal vector. Extracting boundary points, the parameters of width and length were obtained by calculating the position relation between boundaries and centroid. The results showed that compared with traditional clustering methods, the proposed dynamic-K-threshold method can accurately segment single leaf points without branch point losses, which ensured the integrity and thoroughness of the clustering results. The extracted parameters based on real 3D spatial information can guarantee the accuracy to a certain extent, which provided basic technical support for evaluation of illumination distribution calculation and automatic pruning.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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