用于果树智能修剪的高效三维重建和骨架提取技术

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Computers and Electronics in Agriculture Pub Date : 2024-11-05 DOI:10.1016/j.compag.2024.109554
Xiaojuan Li , Bo Liu , Yinggang Shi , Mingming Xiong , Dongyu Ren , Letian Wu , Xiangjun Zou
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引用次数: 0

摘要

果树的三维重建在评估果树生长状况、分析农艺性状以及对果树器官进行分类方面发挥着至关重要的作用。这对于实施果园智能化管理至关重要。本研究旨在开发一种经济高效的果树三维重建和骨架提取方法。所提出的方法利用了飞行时间(TOF)传感器捕捉到的三维几何结构,并解决了遮挡和透视模糊等常见问题。首先,利用 TOF 传感器及其配套组件搭建采集平台,采集果树生长关键期的全范围点云。通过点云预处理操作过滤噪声信息,获得完整的目标点云,并提取其结构不变特征。引入 IWOA-RANSAC-NDT 算法进行三维模型配准。其次,利用 Delaunay 三角测量算法和 Dijkstra 最短路径算法计算最小生成树。利用 Kd 树数据结构加快了分支分割。使用 Levenberg Marquardt 算法和圆柱拟合方法获得完整的果树骨架模型。最后,以核桃树为实验对象,构建了高精度果树点云模型,并根据测量数据进行了实际验证。研究结果表明,所提出的方法可以精确地构建果树的三维点云和骨架模型,与测量数据的精度偏差保持在 7%以内。所提出的方法为未来开发高度自主、实用和面向用户的果树修剪系统提供了宝贵的数据和技术支持。
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Efficient three-dimensional reconstruction and skeleton extraction for intelligent pruning of fruit trees
The three-dimensional reconstruction of fruit trees plays a crucial role in assessing their growth status, analyzing agronomic traits, and categorizing their organs. This is vital for implementing intelligent orchard management. This study aims to develop a cost-effective and efficient method for the three-dimensional reconstruction and skeleton extraction of fruit trees. The proposed method leverages the 3D geometric structure captured by Time-of-Flight (TOF) sensors and addresses common issues such as occlusion and perspective ambiguity. Firstly, the TOF sensor and its supporting components are used to build an acquisition platform to collect the full range point cloud of fruit trees in the key growth period. The noise information is filtered through the point cloud preprocessing operation to obtain the complete target point cloud and extract its structural invariant features. The IWOA-RANSAC-NDT algorithm is introduced for 3D model registration. Secondly, the Delaunay triangulation algorithm and Dijkstra shortest path algorithm are used to calculate the Minimum Spanning Tree. Branch segmentation is expedited using the Kd-tree data structure. The Levenberg Marquardt algorithm and the cylindrical fitting method are used to obtain the full fruit tree skeleton model. Finally, taking walnut tree as the experimental object, a high-precision fruit tree point cloud model is constructed, and the actual verification is carried out based on the measured data. Findings indicate that the proposed methodology can accurately construct both 3D point cloud and skeleton models of fruit trees with accuracy deviations from the measured data remaining within 7 %. The proposed method offers valuable data and technical support for the future development of highly autonomous, practical, and user-oriented fruit tree pruning systems.
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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
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