Apple tree architectural trait phenotyping with organ-level instance segmentation from point cloud

被引:1
|
作者
Jiang, Lizhi [1 ,2 ]
Li, Changying [2 ]
Fu, Longsheng [1 ]
机构
[1] Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Shaanxi, Peoples R China
[2] Univ Florida, Dept Agr & Biol Engn, Biosensing Automat & Intelligence Lab, Gainesville, FL 32611 USA
基金
中国国家自然科学基金;
关键词
Plant phenotyping; Apple tree; 3D segmentation; PointNeXt; Point Transformer V2; SoftGroup plus plus; PLANT-PART SEGMENTATION; PHOTOGRAMMETRY;
D O I
10.1016/j.compag.2024.109708
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
Three-dimensional (3D) plant phenotyping techniques measure organ-level traits effectively and provide detailed plant growth information to breeders. In apple tree breeding, architectural traits can determine photosynthesis efficiency and characterize the developmental stages of trees. The overall goal of this study was to develop a deep learning-based organ-level instance segmentation method to quantify the 3D architectural traits of apple trees. This study utilized PointNeXt for the semantic segmentation of apple tree point clouds, classifying them into trunks and branches, and benchmarked its performance against several competitive models, including PointNet, PointNet++, and Point Transformer V2 (PTv2). A cylinder-based constraint method was introduced to refine the semantic segmentation results. Next, the branches were identified with the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The type of 3D skeleton vertices determined whether a cluster represented a single branch or multiple branches. If multiple, a graph-based technique further separated them. This study also directly applied the instance segmentation model SoftGroup++ to the apple tree point clouds and analyzed the segmentation results on the apple tree dataset. Finally, seven architectural traits of apple trees were extracted, including height, volume, and crown width of the tree, as well as height and diameter for the trunk, and length and count for the branches. The experimental results showed that the post-processed mIoU values for PointNet, PointNet++, PTv2, and PointNeXt were 0.8495, 0.8535, 0.9500, and 0.9481, respectively. The final instance segmentation results based on SoftGroup++ and PointNeXt achieved mAP_50 of 0.815 and 0.842, respectively. For traits such as tree height, trunk length and diameter, branch length, and branch count, the method based on PointNeXt achieved R2 values of 0.987, 0.788, 0.877, 0.796, and 0.934, with mean absolute percentage errors of 0.86 %, 2.17 %, 5.93 %, 10.24 %, and 13.55 %, respectively. The segmentation results of PTv2 and SoftGroup++ were also used to extract the phenotypic traits of apple trees, achieving results comparable to those of PointNeXt. The proposed method demonstrates a cost-effective and accurate approach for extracting the architectural traits of apple trees, which will benefit apple breeding programs as well as the precision management of apple orchards.
引用
收藏
页数:16
相关论文
共 19 条
  • [1] ORGAN-LEVEL INSTANCE SEGMENTATION OF OILSEED RAPE AT SEEDLING STAGE BASED ON 3D POINT CLOUD
    Li, Jie
    Li Qingqing
    Qiao, Jiangwei
    Li, Li
    Yao, Jian
    Tu, Jingmin
    APPLIED ENGINEERING IN AGRICULTURE, 2024, 40 (02) : 151 - 164
  • [2] Delving into the Potential of Deep Learning Algorithms for Point Cloud Segmentation at Organ Level in Plant Phenotyping
    Xie, Kai
    Zhu, Jianzhong
    Ren, He
    Wang, Yinghua
    Yang, Wanneng
    Chen, Gang
    Lin, Chengda
    Zhai, Ruifang
    REMOTE SENSING, 2024, 16 (17)
  • [3] Tensor-based classification and segmentation of three-dimensional point clouds for organ-level plant phenotyping and growth analysis
    Elnashef, Bashar
    Filin, Sagi
    Lati, Ran Nisim
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2019, 156 : 51 - 61
  • [4] POINT CLOUD SEGMENTATION USING HIERARCHICAL TREE FOR ARCHITECTURAL MODELS
    Hassaan, Omair
    Shamail, Abeera
    Butt, Zain
    Taj, Murtaza
    2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2019, : 1582 - 1586
  • [5] Automatic organ-level point cloud segmentation of maize shoots by integrating high-throughput data acquisition and deep learning
    Li, Yinglun
    Wen, Weiliang
    Miao, Teng
    Wu, Sheng
    Yu, Zetao
    Wang, Xiaodong
    Guo, Xinyu
    Zhao, Chunjiang
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2022, 193
  • [6] Automatic Segmentation of Tree Structure From Point Cloud Data
    Digumarti, Sundara Tejaswi
    Nieto, Juan
    Cadena, Cesar
    Siegwart, Roland
    Beardsley, Paul
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2018, 3 (04): : 3043 - 3050
  • [7] Organ-level instance segmentation enables continuous time-space-spectrum analysis of pre-clinical abdominal photoacoustic tomography images
    Liang, Zhichao
    Zhang, Shuangyang
    Mo, Zongxin
    Zhang, Xiaoming
    Wei, Anqi
    Chen, Wufan
    Qi, Li
    MEDICAL IMAGE ANALYSIS, 2025, 101
  • [8] Deep Learning-Based Plant Organ Segmentation and Phenotyping of Sorghum Plants Using LiDAR Point Cloud
    Patel, Ajay Kumar
    Park, Eun-Sung
    Lee, Hongseok
    Priya, G. G. Lakshmi
    Kim, Hangi
    Joshi, Rahul
    Arief, Muhammad Akbar Andi
    Kim, Moon S.
    Baek, Insuck
    Cho, Byoung-Kwan
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2023, 16 : 8492 - 8507
  • [9] Individual tree segmentation from a leaf-off photogrammetric point cloud
    Carr, Julia C.
    Slyder, Jacob B.
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2018, 39 (15-16) : 5195 - 5210
  • [10] Estimating Tree Structural Parameters via Automatic Tree Segmentation From LiDAR Point Cloud Data
    Itakura, Kenta
    Miyatani, Satoshi
    Hosoi, Fumiki
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2022, 15 : 555 - 564