Obtaining growth information of peanut in fields based on 3D LiDAR perception

HU Lian; LIU Yuxuan; ZANG Ying; HE Jie; WANG Pei; HUANG Junwei; HUANG Peikui; ZHAO Runmao

doi:10.13300/j.cnki.hnlkxb.2025.04.010

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Obtaining growth information of peanut in fields based on 3D LiDAR perception

Crop genetic improvement and cultivation | 更新时间：2026-11-05

- Obtaining growth information of peanut in fields based on 3D LiDAR perception
- Journal of Huazhong Agricultural University Vol. 44, Issue 4, Pages: 102-112(2025)
- 作者机构：
  
  华南农业大学工程学院，广州 510642
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2025.04.010
  CLC： S565.2;S24
- Received：01 May 2024，
  
  Published：05 July 2025
- 稿件说明：
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胡炼，刘于轩，臧英，等.基于3D LiDAR感知的大田花生长势信息获取［J］.华中农业大学学报，2025，44（4）：102‐112.

HU Lian,LIU Yuxuan,ZANG Ying,et al.Obtaining growth information of peanut in fields based on 3D LiDAR perception[J].Journal of Huazhong Agricultural University,2025,44(04):102-112.
胡炼，刘于轩，臧英，等.基于3D LiDAR感知的大田花生长势信息获取［J］.华中农业大学学报，2025，44（4）：102‐112. DOI： 10.13300/j.cnki.hnlkxb.2025.04.010.

HU Lian,LIU Yuxuan,ZANG Ying,et al.Obtaining growth information of peanut in fields based on 3D LiDAR perception[J].Journal of Huazhong Agricultural University,2025,44(04):102-112. DOI： 10.13300/j.cnki.hnlkxb.2025.04.010.

摘要

为实现大田环境下快速准确获取作物株高、冠幅等长势信息，以花生为研究对象，采用3D LiDAR感知技术获取大田花生点云数据，经配准、去噪等处理，构建三维点云模型；基于KD-TREE的点云植株分割算法对大田花生植株点云数据进行单株分割，采用体素网格法估计植株体积、旋转卡壳法提取株高和最大冠幅，获取花生长势信息；在花生种植试验区域分别采集3个不同时期花生植株得到点云数据，采用3D LiDAR感知技术开展花生单株分割和提取株高与最大冠幅的验证试验，考察长势信息获取精度，采用召回率与精确率对结果进行精度评价。结果显示，大田花生单株分割的召回率与精确率均可达84%以上，表明该方法应用于大田花生点云数据分割具有较好的准确性与完整性；将提取的花生株高与最大冠幅等参数与人工测量值进行对比，3个时期植株高度的平均绝对百分比误差分别为6.271%、4.368%和4.986%，最大冠幅的平均绝对百分比误差分别为7.114%、5.606%和4.541%，株高均方根误差分别为0.010、0.015和0.027 m，最大冠幅均方根误差分别为0.011 、0.020和0.021 m；株高数据线性回归决定系数分别为0.888、0.951和0.842，最大冠幅数据线性回归决定系数分别为0.934、0.932和0.927，表明使用点云测量可实现花生表型参数高精度、非破坏性提取。

Abstract

Peanut was used to rapidly and accurately obtain crop growth information including plant height and canopy width in the field.3D LiDAR perception technology was used to obtain the point cloud data of peanut in the field.A 3D point cloud model was constructed through registration， denoising， and other processing of the point cloud data.The point cloud plant segmentation method based on KD-TREE was used to segment the point cloud data of individual peanut plants.The convex hull algorithm was used to estimate plant volume and the rotating caliper method was used to extract plant height and maximum canopy width to obtain the growth information of peanut.Point cloud data of peanut plants at three different stages of growth were collected in a peanut planting area.The method proposed was used to carry out verification tests for the segmentation of individual peanut plant and the extraction of plant height and maximum canopy width.The accuracy of obtaining growth information was investigated.The accuracy of the results was evaluated with recall rate and precision rate.The results showed that the recall rate and precision rate of the segmentation of individual peanut plant in the field was over 85%， indicating that the method proposed has good accuracy and completeness for segmenting point cloud data of peanut in the field.The extracted parameters including plant height and maximum canopy width of peanut were compared with those of manual measurements.The average absolute percentage error of plant height and maximum canopy width at three different stages of growth was 6.271%， 4.368%， 4.986%， and 7.114%， 5.606%， 4.541%， with the root mean square error of 0.010， 0.015， 0.027 m， and 0.011， 0.020， 0.021 m， respectively.The linear regression determination coefficient of plant height and maximum canopy width was 0.888， 0.951， 0.842， and 0.934， 0.932， 0.927， respectively.It is indicated that the use of point cloud measurement can achieve high-precision and non-destructive extraction of phenotypic parameters for peanut in the field.It will provide important technical support for the cultivation and breeding of peanut.

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references

张越，翟福琪，蔡孙宝，等.基于点云数据的植物叶片特征提取及三维重建［J］.中国测试，2021，47（8）：6-12.ZHANG Y，ZHAI F Q，CAI S B，et al.Feature extraction and 3D reconstruction of plant leaf based on point cloud data［J］.China measurement & test，2021，47（8）：6-12 （in Chinese with English abstract） .

钟旭升.基于深度学习的植株点云分割方法研究［D］.广州：广东工业大学，2022.ZHONG X S.Research on plant point cloud segmentation methods based on deep learning［D］.Guangzhou：Guangdong University of Technology，2022 （in Chinese with English abstract） .

周猛祥.基于Kinect v2的花生冠层结构三维重建方法及表型信息获取系统研究［D］.保定：河北农业大学，2022.ZHOU M X.Research on 3D reconstruction method and phenotype information acquisition system of peanut canopy structure based on Kinect v2［D］.Baoding：Heibei Agricultural University，2022 （in Chinese with English abstract） .

胡松涛.基于多源数据的马铃薯植株表型参数提取方法研究［D］.武汉：华中农业大学，2023.HU S T.Research on the extraction method of potato plant phenotype parameters based on multi-source data［D］.Wuhan：Huazhong Agricultural University，2023 （in Chinese with English abstract） .

彭程，李帅，苗艳龙，等.基于三维点云的番茄植株茎叶分割与表型特征提取［J］.农业工程学报，2022，38（9）：187-194.PENG C，LI S，MIAO Y L，et al.Stem-leaf segmentation and phenotypic trait extraction of tomatoes using three-dimensional point cloud［J］.Transactions of the CSAE，2022，38（9）：187-194 （in Chinese with English abstract） .

ZAMAN Q U，SCHUMANN A W，HOSTLER H K.Quantifying sources of error in ultrasonic measurements of citrus orchards［J］.Applied engineering in agriculture，2007，23（4）：449-453.

李霞，王维新，宋海堂.基于超声波技术棉株高度自动测量的研究［J］.农机化研究，2008，30（6）：187-189.LI X，WANG W X，SONG H T.Research of cotton altitude automation measurement based on ultrasonic technology［J］.Journal of agricultural mechanization research，2008，30（6）：187-189 （in Chinese with English abstract） .

徐文兵，程效军.植物表面特征对三维激光点云的影响［J］.激光与光电子学进展，2020，57（24）：382-388.XU W B，CHENG X J.Impact of plant surface features on 3D laser point cloud［J］.Laser & optoelectronics progress，2020，57（24）：382-388 （in Chinese with English abstract） .

TEN HARKEL J，BARTHOLOMEUS H，KOOISTRA L.Biomass and crop height estimation of different crops using UAV-based LIDAR［J/OL］.Remote sensing，2020，12（1）：17［2024-05-01］.https：//doi.org/10.3390/rs12010017 .

张亚倩.无人机激光雷达反演玉米与大豆高度及叶面积指数［D］.福州：福建农林大学，2022.ZHANG Y Q.Inversion of corn and soybean heights and leaf area index using UAV LiDAR［D］.Fuzhou：Fujiang Agriculture and Forestry University，2022 （in Chinese with English abstract） .

杨楠，周萌，陈欢，等.基于无人机RGB影像的小麦叶面积指数与产量估算研究［J］.麦类作物学报，2023，43（7）：920-932.YANG N，ZHOU M，CHEN H，et al.Estimation of wheat leaf area index and yield based on UAV RGB images［J］.Journal of triticeae crops，2023，43（7）：920-932 （in Chinese with English abstract） .

周志宇.基于地基激光雷达点云的加拿大一枝黄花避荫反应研究［D］.南京：南京大学，2019.ZHOU Z Y.Study on the shade avoidance response of solidago canadensis based on terrestrial LiDAR point clouds［D］.Nanjing：Nanjing University，2019 （in Chinese with English abstract） .

徐卫红，彭家真，高红旗.改进迭代最近点算法支持下的LiDAR点云与正射影像粗-精配准方法［J］.江西科学，2022，40（3）：539-544.A method for coarse-to-fine registration of LiDAR point clouds and orthophotos supported by an improved iTERATIVE cLOSEST pOInt （ICP） algorithm［J］.Jiangxi science，2022，40（3）：539-544 （in Chinese with English abstract） .

SCHRADER J，PILLAR G，KREFT H.Leaf-IT：an Android application for measuring leaf area［J］.Ecology and evolution，2017，7（22）：9731-9738.

蔡双泽.基于无人车的温室生菜三维点云获取方法及表型解析技术研究［D］.镇江：江苏大学，2023.CAI S Z.Research on 3D point cloud acquisition method and phenotype analysis technology of greenhouse lettuce based on unmanned vehicles［J］.Zhenjiang：Jiangsu University，2023 （in Chinese with English abstract） .

陈昆.基于激光点云的油菜数据处理方法研究［D］.武汉：华中农业大学，2019.CHEN K.Research on rapeseed data processing method based on laser point cloud［D］.Wuhan：Huazhong Agricultural University，2019 （in Chinese with English abstract） .

樊正强.基于机器人移动平台的大田玉米高通量表型参数获取研究［D］.杨凌：西北农林科技大学，2022.FAN Z Q.Research on high-throughput phenotyping parameter acquisition of maize in large fields based on robot mobile platform［D］.Yangling：Northwest A & F University，2022 （in Chinese with English abstract） .

关海鸥，许少华，马晓丹.植物叶面积无损测量预处理技术研究［J］.农机化研究，2010，32（3）：171-173.GUAN H O，XU S H，MA X D.Study on the non-loss measurement pre-processing technology of plant leaf area［J］.Journal of agricultural mechanization research，2010，32（3）：171-173 （in Chinese with English abstract） .

黄金其，黄晶晶，谢景鑫，等.基于移动式三维激光雷达的柑橘冠层结构信息的获取［J］.湖南农业大学学报（自然科学版），2022，48（1）：109-113.HUANG J Q，HUANG J J，XIE J X，et al.Research on obtaining information of orange tree structure based on mobile 3D lidar［J］.Journal of Hunan Agricultural University （natural sciences），2022，48（1）：109-113 （in Chinese with English abstract） .

姜友谊，张成健，韩少宇，等.基于无人机三维点云的玉米植株自动计数研究［J］.浙江农业学报，2022 ，34（9）：2032-2042.JIANG Y Y，ZHANG C J，HAN S Y，et al.Automatic counting of maize plants based on unmanned aerial vehicle （UAV） 3D point cloud［J］.Acta agriculturae Zhejiangensis，2022，34（9）：2032-2042 （in Chinese with English abstract） .

LANGE S，SÜNDERHAUF N，PROTZEL P.Autonomous landing for a multirotor UAV using vision［M］.Venice：SIMPAR，2008：482-491.

LIU Z H，JIN S C，LIU X Q，et al.Extraction of wheat spike phenotypes from field-collected lidar data and exploration of their relationships with wheat yield［J］.IEEE transactions on geoscience and remote sensing，2023，61：1-13.

KUMAR A，YOON S，SANAL KUMAR V R.Mixed reality simulation of high-endurance unmanned aerial vehicle with dual-head electromagnetic propulsion devices for earth and other planetary explorations［J/OL］.Applied sciences，2020，10（11）：3736［2024-05-01］.https：//doi.org/10.3390/app10113736..

李涛，翟秀丽，贾红宾.大田玉米图像植株分割方法研究［J］.河南工程学院学报（自然科学版），2024，36（1）：60-64.LI T，ZHAI X L，JIA H B.Group plants segmentation algorithm research based on field maize images［J］.Journal of Hennan Institute of Engineering （natural science edition），2024，36（1）：60-64 （in Chinese with English abstract） .

孙亚军.基于无人机多光谱的花生长势监测及估产［D］.泰安：山东农业大学，2023.SUN Y J.Peanut growth monitoring and yield estimation based on drone multispectral technology［D］.Taian：Shandong Agricultural University，2023 （in Chinese with English abstract） .

DALPONTE M，BRUZZONE L，GIANELLE D.Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data［J］.Remote sensing of environment，2012，123：258-270.

史双月.基于三维点云的油菜植株形态结构性状提取研究［D］.扬州：扬州大学，2023.SHI S Y.Research on extracting morphological and structural characteristics of rapeseed plants based on 3D point cloud［D］.Yangzhou：Yangzhou University，2023 （in Chinese with English abstract） .

宋晨旭，于翀宇，邢永超，等.基于OpenCV的大豆籽粒多表型参数获取算法［J］.农业工程学报，2022，38（20）：156-163.SONG C X，YU C Y，XING Y C，et al.Algorith for acquiring multi-phenotype parameters of soybean seed based on OpenCV［J］.Transactions of the CSAE，2022，38（20）：156-163 （in Chinese with English abstract） .

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