Deep Learning-Based Plant Organ Segmentation and Phenotyping of Sorghum Plants Using LiDAR Point Cloud
Increasing food demands, global climatic variations, and population growth have spurred the growth of crop yield driven by plant phenotyping in the age of Big Data. High-throughput phenotyping of sorghum at each plant and organ level is vital in molecular plant breeding to increase crop yield. LiDAR...
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| Format: | Article |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10243159/ |
| _version_ | 1827198456717901824 |
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| author | Ajay Kumar Patel Eun-Sung Park Hongseok Lee G. G. Lakshmi Priya Hangi Kim Rahul Joshi Muhammad Akbar Andi Arief Moon S. Kim Insuck Baek Byoung-Kwan Cho |
| author_facet | Ajay Kumar Patel Eun-Sung Park Hongseok Lee G. G. Lakshmi Priya Hangi Kim Rahul Joshi Muhammad Akbar Andi Arief Moon S. Kim Insuck Baek Byoung-Kwan Cho |
| author_sort | Ajay Kumar Patel |
| collection | DOAJ |
| description | Increasing food demands, global climatic variations, and population growth have spurred the growth of crop yield driven by plant phenotyping in the age of Big Data. High-throughput phenotyping of sorghum at each plant and organ level is vital in molecular plant breeding to increase crop yield. LiDAR (light detection and ranging) sensor provides 3-D point clouds of plants with the advantages of high precision, high resolution, and rapid measurement. However, need to develop robust algorithms for extracting the phenotypic traits of sorghum plants using LiDAR 3-D point cloud. This study utilized four 3-D point cloud-based deep learning models named PointNet, PointNet++, PointCNN, and dynamic graph CNN for the specific objective of the segmentation of sorghum plants. Subsequently, phenotypic traits were extracted using the segmentation results. Study plants sample were grown under controlled conditions at various developmental stages. The extracted phenotypic traits outcome has been validated through the manually measured phenotypic traits of the sorghum plant. PointNet++ outperformed the other three deep learning models and provided the best segmentation result with a mean accuracy of 91.5%. The correlations of the six phenotypic traits, such as plant height, plant crown diameter, plant compactness, stem diameter, panicle length, and panicle width were calculated from the segmentation results of the PointNet++ model and the measured coefficient of determination (<italic>R</italic><sup>2</sup>) were 0.97, 0.96, 0.94, 0.90, 0.95, and 0.88, respectively. The obtained results showed that LiDAR 3-D point cloud have good potential to measure the sorghum plant phenotype traits rapidly and accurately using deep learning techniques. |
| first_indexed | 2024-03-08T07:19:39Z |
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| institution | Directory Open Access Journal |
| issn | 1939-1404 2151-1535 |
| language | English |
| last_indexed | 2025-03-21T10:17:19Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-b4e26e31fe5847818bf725f367e16bc32024-07-03T23:00:11ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352023-01-01168492850710.1109/JSTARS.2023.331281510243159Deep Learning-Based Plant Organ Segmentation and Phenotyping of Sorghum Plants Using LiDAR Point CloudAjay Kumar Patel0https://orcid.org/0000-0002-1938-3318Eun-Sung Park1https://orcid.org/0000-0001-6826-2865Hongseok Lee2https://orcid.org/0000-0003-0571-0522G. G. Lakshmi Priya3https://orcid.org/0000-0001-5322-1518Hangi Kim4https://orcid.org/0000-0002-2985-5446Rahul Joshi5https://orcid.org/0000-0002-5834-2893Muhammad Akbar Andi Arief6https://orcid.org/0009-0003-7457-4859Moon S. Kim7https://orcid.org/0000-0001-8504-9839Insuck Baek8https://orcid.org/0000-0003-1044-349XByoung-Kwan Cho9https://orcid.org/0000-0002-8397-9853Department of Smart Agricultural Systems, College of Agricultural and Life Science, Chungnam National University, Daejeon, South KoreaDepartment of Smart Agricultural Systems, College of Agricultural and Life Science, Chungnam National University, Daejeon, South KoreaNational Institute of Crop Science, Rural Development Administration, Miryang, South KoreaDepartment of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South KoreaDepartment of Smart Agricultural Systems, College of Agricultural and Life Science, Chungnam National University, Daejeon, South KoreaDepartment of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South KoreaDepartment of Smart Agricultural Systems, College of Agricultural and Life Science, Chungnam National University, Daejeon, South KoreaAgricultural Research Service, United States Department of Agriculture, Environmental Microbial and Food Safety Laboratory, Beltsville, MD, USAAgricultural Research Service, United States Department of Agriculture, Environmental Microbial and Food Safety Laboratory, Beltsville, MD, USADepartment of Smart Agricultural Systems, College of Agricultural and Life Science, Chungnam National University, Daejeon, South KoreaIncreasing food demands, global climatic variations, and population growth have spurred the growth of crop yield driven by plant phenotyping in the age of Big Data. High-throughput phenotyping of sorghum at each plant and organ level is vital in molecular plant breeding to increase crop yield. LiDAR (light detection and ranging) sensor provides 3-D point clouds of plants with the advantages of high precision, high resolution, and rapid measurement. However, need to develop robust algorithms for extracting the phenotypic traits of sorghum plants using LiDAR 3-D point cloud. This study utilized four 3-D point cloud-based deep learning models named PointNet, PointNet++, PointCNN, and dynamic graph CNN for the specific objective of the segmentation of sorghum plants. Subsequently, phenotypic traits were extracted using the segmentation results. Study plants sample were grown under controlled conditions at various developmental stages. The extracted phenotypic traits outcome has been validated through the manually measured phenotypic traits of the sorghum plant. PointNet++ outperformed the other three deep learning models and provided the best segmentation result with a mean accuracy of 91.5%. The correlations of the six phenotypic traits, such as plant height, plant crown diameter, plant compactness, stem diameter, panicle length, and panicle width were calculated from the segmentation results of the PointNet++ model and the measured coefficient of determination (<italic>R</italic><sup>2</sup>) were 0.97, 0.96, 0.94, 0.90, 0.95, and 0.88, respectively. The obtained results showed that LiDAR 3-D point cloud have good potential to measure the sorghum plant phenotype traits rapidly and accurately using deep learning techniques.https://ieeexplore.ieee.org/document/10243159/3-D point clouddeep learninglidar techniquephenotypingsorghum |
| spellingShingle | Ajay Kumar Patel Eun-Sung Park Hongseok Lee G. G. Lakshmi Priya Hangi Kim Rahul Joshi Muhammad Akbar Andi Arief Moon S. Kim Insuck Baek Byoung-Kwan Cho Deep Learning-Based Plant Organ Segmentation and Phenotyping of Sorghum Plants Using LiDAR Point Cloud IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3-D point cloud deep learning lidar technique phenotyping sorghum |
| title | Deep Learning-Based Plant Organ Segmentation and Phenotyping of Sorghum Plants Using LiDAR Point Cloud |
| title_full | Deep Learning-Based Plant Organ Segmentation and Phenotyping of Sorghum Plants Using LiDAR Point Cloud |
| title_fullStr | Deep Learning-Based Plant Organ Segmentation and Phenotyping of Sorghum Plants Using LiDAR Point Cloud |
| title_full_unstemmed | Deep Learning-Based Plant Organ Segmentation and Phenotyping of Sorghum Plants Using LiDAR Point Cloud |
| title_short | Deep Learning-Based Plant Organ Segmentation and Phenotyping of Sorghum Plants Using LiDAR Point Cloud |
| title_sort | deep learning based plant organ segmentation and phenotyping of sorghum plants using lidar point cloud |
| topic | 3-D point cloud deep learning lidar technique phenotyping sorghum |
| url | https://ieeexplore.ieee.org/document/10243159/ |
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