| 基于毫米波雷达的果园单木冠层信息提取 |
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| 引用本文： | 杨洲,李洋,段洁利,徐兴,余家祥,申东英,袁浩天.基于毫米波雷达的果园单木冠层信息提取[J].农业工程学报,2021,37(21):173-182. |
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| 作者姓名： | 杨洲 李洋 段洁利 徐兴 余家祥 申东英 袁浩天 |
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| 作者单位： | 1. 华南农业大学工程学院，广州 510642； 3. 嘉应学院广东省山区特色农业资源保护与精准利用重点实验室，梅州 514015;2. 华南农业大学电子工程学院，广州 510642； |
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| 基金项目： | 广东省重点领域研发计划项目（2019B020223002）；国家重点研发计划项目（2020YFD1000104）；财政部和农业农村部：国家现代农业产业技术体系建设专项（CARS-31-10）；广东省教育厅科研项目(2020KZDZX1036)。 |
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| 摘 要： | 为解决当前果园探测技术难以在恶劣的果园环境中提取果树冠层信息的问题。该研究将毫米波雷达应用于果园冠层探测，搭建了基于毫米波雷达的果园冠层探测系统，利用该系统扫描得到了果园点云，检测和估算得到每棵果树的株高、冠幅和体积参数。针对毫米波雷达在不同距离下产生点云密度不同的问题，该研究提出了一种基于可变轴的椭球模型自适应密度聚类算法，用以提高果树点云识别效果，进而使用Alpha-shape算法和随机抽样一致算法（Random Sample Consensus）对果树进行了表面重建和结构参数的提取。通过与人工测量数据比较，该研究提出的聚类算法可以有效的识别和提取单木冠层点云，代表果树识别精度的 F1 分数为 93.7%；检测到的果树的株高和冠幅的平均相对误差分别为8.7%和8.1%，决定系数分别为0.84和0.92，均方根误差分别为16.39和7.82 cm；使用Alpha-shape算法计算得到平均果树体积为5.6 m3，相比传统几何法测量体积，体积计算准确度提高了59.4%。该研究表明毫米波雷达可以用于果园冠层信息的准确提取，为采集果园冠层信息提供了技术，对农业信息采集和自动化作业技术的发展具有重要意义。
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| 关 键 词： | 雷达 机械化 冠层信息 点云处理 密度聚类 单木识别 |
| 收稿时间： | 2021/7/13 0:00:00 |
| 修稿时间： | 2021/9/16 0:00:00 |
Extraction of the crown information of single tree in orchard based on millimeter wave radar |
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| Yang Zhou,Li Yang,Duan Jieli,Xu Xing,Yu Jiaxiang,Shen Dongying,Yuan Haotian.Extraction of the crown information of single tree in orchard based on millimeter wave radar[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(21):173-182. |
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| Authors: | Yang Zhou Li Yang Duan Jieli Xu Xing Yu Jiaxiang Shen Dongying Yuan Haotian |
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| Institution: | 1. College of Engineering, South China Agricultural University, Guangzhou 510642, China;3. Guangdong Provincial Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, Jiaying University, Meizhou 514015, China;2. College of Electronic Engineering,South China Agricultural University, Guangzhou 510642, China; |
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| Abstract: | Abstract: An accurate and rapid extraction of fruit tree canopy has been one of the most important steps to determine the precise application of pesticides in modern agriculture. However, the current detection cannot fully meet the harsh requirements in the complex environment of an orchard in recent years. In this study, a novel extraction system was proposed to identify the fruit tree canopy in an orchard using millimeter-wave radar. Firstly, the region of interest (ROI) was set to remove a large number of environmental point clouds, and then the random sampling was used to fit and segment the ground point clouds. Secondly, a density-based clustering was selected to integrate the resolution of millimeter-wave radar and the distance parameter of the point cloud. An adaptive density clustering was then established to deal with the different densities of the point cloud at different distances when using the millimeter-wave radar. As such, the neighborhood radius was adaptively adjusted in different directions, according to the distance from the point cloud to the radar. The point cloud density was then used to identify the fruit tree canopy, further improving the recognition performance of the point cloud. Finally, an Alpha-shape was selected to reconstruct the three-dimensional surface of the fruit tree. The optimal parameters of the three-dimensional reconstruction were achieved to evaluate the volume from Alpha values and the geometric measurement. The random sampling was also used to extract the structural parameters. The parameters of the mathematical model were estimated from a data set with a large number of outliers using multiple iterations. A curve model was then obtained closer to the actual situation. In addition, a field test was conducted to verify the feasibility of the model in the Loquat Plant Resource Garden (113.367789°E, 23.164129°N) of South China Agricultural University on April 2, 2021. 40 yellow-bark fruit trees were also scanned in the test. The results showed that a higher performance was achieved to effectively identify and extract the point clouds of a single tree canopy, where the F1 score was 93.7% for the recognition accuracy of fruit trees. Furthermore, the average relative errors of the plant height and crown width in the fruit trees were 8.7% and 8.1%, respectively, while the coefficients of determination were 0.84 and 0.92, respectively, and the root mean square errors were 16.39 and 7.82, respectively, compared with the manual measurement. In addition, the average volume of fruit trees was 5.6 m? using Alpha-shape, increasing by 59.4% in the accuracy of volume, compared with the traditional. Nevertheless, two recommendations can be addressed during this time: 1) To identify the fruit trees with the overlapping canopies using a relatively higher resolution of millimeter-wave radar; 2) To quantitatively extract the volume of the fruit tree in the harsh sense. Anyway, the millimeter-wave radar can be widely expected to accurately extract the canopy information in an orchard. The finding can provide a new promising technology to extract the canopy information for the data collection and automatic operation in modern agriculture. |
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| Keywords: | radar mechanization crown information point cloud processing density clustering single tree recognition |
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