In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR
Plant breeding programs and a wide range of plant science applications would greatly benefit from the development of in-field high throughput phenotyping technologies. In this study, a terrestrial LiDAR-based high throughput phenotyping system was developed. A 2D LiDAR was applied to scan plants fro...
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doaj-e4a76677cfe64a08b167855151dde7662020-11-25T02:46:55ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2018-01-01910.3389/fpls.2018.00016315910In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDARShangpeng Sun0Changying Li1Andrew H. Paterson2Andrew H. Paterson3Yu Jiang4Rui Xu5Jon S. Robertson6John L. Snider7Peng W. Chee8School of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA, United StatesSchool of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA, United StatesDepartment of Crop and Soil Sciences, College of Agricultural and Environmental Sciences, University of Georgia, Athens, GA, United StatesDepartment of Genetics, Franklin College of Arts and Sciences, University of Georgia, Athens, GA, United StatesSchool of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA, United StatesSchool of Electrical and Computer Engineering, College of Engineering, University of Georgia, Athens, GA, United StatesDepartment of Crop and Soil Sciences, College of Agricultural and Environmental Sciences, University of Georgia, Athens, GA, United StatesDepartment of Crop and Soil Sciences, College of Agricultural and Environmental Sciences, University of Georgia, Athens, GA, United StatesDepartment of Crop and Soil Sciences, College of Agricultural and Environmental Sciences, University of Georgia, Athens, GA, United StatesPlant breeding programs and a wide range of plant science applications would greatly benefit from the development of in-field high throughput phenotyping technologies. In this study, a terrestrial LiDAR-based high throughput phenotyping system was developed. A 2D LiDAR was applied to scan plants from overhead in the field, and an RTK-GPS was used to provide spatial coordinates. Precise 3D models of scanned plants were reconstructed based on the LiDAR and RTK-GPS data. The ground plane of the 3D model was separated by RANSAC algorithm and a Euclidean clustering algorithm was applied to remove noise generated by weeds. After that, clean 3D surface models of cotton plants were obtained, from which three plot-level morphologic traits including canopy height, projected canopy area, and plant volume were derived. Canopy height ranging from 85th percentile to the maximum height were computed based on the histogram of the z coordinate for all measured points; projected canopy area was derived by projecting all points on a ground plane; and a Trapezoidal rule based algorithm was proposed to estimate plant volume. Results of validation experiments showed good agreement between LiDAR measurements and manual measurements for maximum canopy height, projected canopy area, and plant volume, with R2-values of 0.97, 0.97, and 0.98, respectively. The developed system was used to scan the whole field repeatedly over the period from 43 to 109 days after planting. Growth trends and growth rate curves for all three derived morphologic traits were established over the monitoring period for each cultivar. Overall, four different cultivars showed similar growth trends and growth rate patterns. Each cultivar continued to grow until ~88 days after planting, and from then on varied little. However, the actual values were cultivar specific. Correlation analysis between morphologic traits and final yield was conducted over the monitoring period. When considering each cultivar individually, the three traits showed the best correlations with final yield during the period between around 67 and 109 days after planting, with maximum R2-values of up to 0.84, 0.88, and 0.85, respectively. The developed system demonstrated relatively high throughput data collection and analysis.http://journal.frontiersin.org/article/10.3389/fpls.2018.00016/fullfield-based high throughput phenotyping3D point cloudmorphologic traitsplant growth analysisLiDAR |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shangpeng Sun Changying Li Andrew H. Paterson Andrew H. Paterson Yu Jiang Rui Xu Jon S. Robertson John L. Snider Peng W. Chee |
spellingShingle |
Shangpeng Sun Changying Li Andrew H. Paterson Andrew H. Paterson Yu Jiang Rui Xu Jon S. Robertson John L. Snider Peng W. Chee In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR Frontiers in Plant Science field-based high throughput phenotyping 3D point cloud morphologic traits plant growth analysis LiDAR |
author_facet |
Shangpeng Sun Changying Li Andrew H. Paterson Andrew H. Paterson Yu Jiang Rui Xu Jon S. Robertson John L. Snider Peng W. Chee |
author_sort |
Shangpeng Sun |
title |
In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR |
title_short |
In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR |
title_full |
In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR |
title_fullStr |
In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR |
title_full_unstemmed |
In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR |
title_sort |
in-field high throughput phenotyping and cotton plant growth analysis using lidar |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Plant Science |
issn |
1664-462X |
publishDate |
2018-01-01 |
description |
Plant breeding programs and a wide range of plant science applications would greatly benefit from the development of in-field high throughput phenotyping technologies. In this study, a terrestrial LiDAR-based high throughput phenotyping system was developed. A 2D LiDAR was applied to scan plants from overhead in the field, and an RTK-GPS was used to provide spatial coordinates. Precise 3D models of scanned plants were reconstructed based on the LiDAR and RTK-GPS data. The ground plane of the 3D model was separated by RANSAC algorithm and a Euclidean clustering algorithm was applied to remove noise generated by weeds. After that, clean 3D surface models of cotton plants were obtained, from which three plot-level morphologic traits including canopy height, projected canopy area, and plant volume were derived. Canopy height ranging from 85th percentile to the maximum height were computed based on the histogram of the z coordinate for all measured points; projected canopy area was derived by projecting all points on a ground plane; and a Trapezoidal rule based algorithm was proposed to estimate plant volume. Results of validation experiments showed good agreement between LiDAR measurements and manual measurements for maximum canopy height, projected canopy area, and plant volume, with R2-values of 0.97, 0.97, and 0.98, respectively. The developed system was used to scan the whole field repeatedly over the period from 43 to 109 days after planting. Growth trends and growth rate curves for all three derived morphologic traits were established over the monitoring period for each cultivar. Overall, four different cultivars showed similar growth trends and growth rate patterns. Each cultivar continued to grow until ~88 days after planting, and from then on varied little. However, the actual values were cultivar specific. Correlation analysis between morphologic traits and final yield was conducted over the monitoring period. When considering each cultivar individually, the three traits showed the best correlations with final yield during the period between around 67 and 109 days after planting, with maximum R2-values of up to 0.84, 0.88, and 0.85, respectively. The developed system demonstrated relatively high throughput data collection and analysis. |
topic |
field-based high throughput phenotyping 3D point cloud morphologic traits plant growth analysis LiDAR |
url |
http://journal.frontiersin.org/article/10.3389/fpls.2018.00016/full |
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