WHEAT EAR DETECTION IN PLOTS BY SEGMENTING MOBILE LASER SCANNER DATA
The use of Light Detection and Ranging (LiDAR) to study agricultural crop traits is becoming popular. Wheat plant traits such as crop height, biomass fractions and plant population are of interest to agronomists and biologists for the assessment of a genotype's performance in the environment. A...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2017-09-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W4/149/2017/isprs-annals-IV-2-W4-149-2017.pdf |
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| author | K. Velumani S. Oude Elberink M. Y. Yang F. Baret |
| author_facet | K. Velumani S. Oude Elberink M. Y. Yang F. Baret |
| author_sort | K. Velumani |
| collection | DOAJ |
| description | The use of Light Detection and Ranging (LiDAR) to study agricultural crop traits is becoming popular. Wheat plant traits such as crop height, biomass fractions and plant population are of interest to agronomists and biologists for the assessment of a genotype's performance in the environment. Among these performance indicators, plant population in the field is still widely estimated through manual counting which is a tedious and labour intensive task. The goal of this study is to explore the suitability of LiDAR observations to automate the counting process by the individual detection of wheat ears in the agricultural field. However, this is a challenging task owing to the random cropping pattern and noisy returns present in the point cloud. The goal is achieved by first segmenting the 3D point cloud followed by the classification of segments into ears and non-ears. In this study, two segmentation techniques: a) voxel-based segmentation and b) mean shift segmentation were adapted to suit the segmentation of plant point clouds. An ear classification strategy was developed to distinguish the ear segments from leaves and stems. Finally, the ears extracted by the automatic methods were compared with reference ear segments prepared by manual segmentation. Both the methods had an average detection rate of 85 %, aggregated over different flowering stages. The voxel-based approach performed well for late flowering stages (wheat crops aged 210 days or more) with a mean percentage accuracy of 94 % and takes less than 20 seconds to process 50,000 points with an average point density of 16  points/cm<sup>2</sup>. Meanwhile, the mean shift approach showed comparatively better counting accuracy of 95% for early flowering stage (crops aged below 225 days) and takes approximately 4 minutes to process 50,000 points. |
| first_indexed | 2024-12-10T16:11:00Z |
| format | Article |
| id | doaj.art-cfafd4e3654e4d7cbde452a460b61b57 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-12-10T16:11:00Z |
| publishDate | 2017-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-cfafd4e3654e4d7cbde452a460b61b572022-12-22T01:42:08ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502017-09-01IV-2-W414915610.5194/isprs-annals-IV-2-W4-149-2017WHEAT EAR DETECTION IN PLOTS BY SEGMENTING MOBILE LASER SCANNER DATAK. Velumani0S. Oude Elberink1M. Y. Yang2F. Baret3Faculty of Geo-Information Science and Earth Observation, University of Twente, the NetherlandsFaculty of Geo-Information Science and Earth Observation, University of Twente, the NetherlandsFaculty of Geo-Information Science and Earth Observation, University of Twente, the NetherlandsUMR EMMAH, INRA, UAPV, Avignon, FranceThe use of Light Detection and Ranging (LiDAR) to study agricultural crop traits is becoming popular. Wheat plant traits such as crop height, biomass fractions and plant population are of interest to agronomists and biologists for the assessment of a genotype's performance in the environment. Among these performance indicators, plant population in the field is still widely estimated through manual counting which is a tedious and labour intensive task. The goal of this study is to explore the suitability of LiDAR observations to automate the counting process by the individual detection of wheat ears in the agricultural field. However, this is a challenging task owing to the random cropping pattern and noisy returns present in the point cloud. The goal is achieved by first segmenting the 3D point cloud followed by the classification of segments into ears and non-ears. In this study, two segmentation techniques: a) voxel-based segmentation and b) mean shift segmentation were adapted to suit the segmentation of plant point clouds. An ear classification strategy was developed to distinguish the ear segments from leaves and stems. Finally, the ears extracted by the automatic methods were compared with reference ear segments prepared by manual segmentation. Both the methods had an average detection rate of 85 %, aggregated over different flowering stages. The voxel-based approach performed well for late flowering stages (wheat crops aged 210 days or more) with a mean percentage accuracy of 94 % and takes less than 20 seconds to process 50,000 points with an average point density of 16  points/cm<sup>2</sup>. Meanwhile, the mean shift approach showed comparatively better counting accuracy of 95% for early flowering stage (crops aged below 225 days) and takes approximately 4 minutes to process 50,000 points.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W4/149/2017/isprs-annals-IV-2-W4-149-2017.pdf |
| spellingShingle | K. Velumani S. Oude Elberink M. Y. Yang F. Baret WHEAT EAR DETECTION IN PLOTS BY SEGMENTING MOBILE LASER SCANNER DATA ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | WHEAT EAR DETECTION IN PLOTS BY SEGMENTING MOBILE LASER
SCANNER DATA |
| title_full | WHEAT EAR DETECTION IN PLOTS BY SEGMENTING MOBILE LASER
SCANNER DATA |
| title_fullStr | WHEAT EAR DETECTION IN PLOTS BY SEGMENTING MOBILE LASER
SCANNER DATA |
| title_full_unstemmed | WHEAT EAR DETECTION IN PLOTS BY SEGMENTING MOBILE LASER
SCANNER DATA |
| title_short | WHEAT EAR DETECTION IN PLOTS BY SEGMENTING MOBILE LASER
SCANNER DATA |
| title_sort | wheat ear detection in plots by segmenting mobile laser scanner data |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W4/149/2017/isprs-annals-IV-2-W4-149-2017.pdf |
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