LiDAR platform for acquisition of 3d plant phenotyping database

Currently, there are no free databases of 3D point clouds and images for seedling phenotyping. Therefore, this paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research. In total, 362 maize seedlings were recorded using a...

Full description

Autores:
Comeche, José Manuel
Murcia, Harold F.
Méndez, Dehyro
Martínez Pérez, Juan Francisco
Tipo de recurso:
Article of journal
Fecha de publicación:
2022
Institución:
Universidad de Ibagué
Repositorio:
Repositorio Universidad de Ibagué
Idioma:
eng
OAI Identifier:
oai:repositorio.unibague.edu.co:20.500.12313/3851
Acceso en línea:
https://hdl.handle.net/20.500.12313/3851
Palabra clave:
3D maize database
3D reconstruction
LiDAR platform
Plant phenotyping
Point clouds
Rights
openAccess
License
http://purl.org/coar/access_right/c_abf2
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network_acronym_str UNIBAGUE2
network_name_str Repositorio Universidad de Ibagué
repository_id_str
dc.title.eng.fl_str_mv LiDAR platform for acquisition of 3d plant phenotyping database
title LiDAR platform for acquisition of 3d plant phenotyping database
spellingShingle LiDAR platform for acquisition of 3d plant phenotyping database
3D maize database
3D reconstruction
LiDAR platform
Plant phenotyping
Point clouds
title_short LiDAR platform for acquisition of 3d plant phenotyping database
title_full LiDAR platform for acquisition of 3d plant phenotyping database
title_fullStr LiDAR platform for acquisition of 3d plant phenotyping database
title_full_unstemmed LiDAR platform for acquisition of 3d plant phenotyping database
title_sort LiDAR platform for acquisition of 3d plant phenotyping database
dc.creator.fl_str_mv Comeche, José Manuel
Murcia, Harold F.
Méndez, Dehyro
Martínez Pérez, Juan Francisco
dc.contributor.author.none.fl_str_mv Comeche, José Manuel
Murcia, Harold F.
Méndez, Dehyro
Martínez Pérez, Juan Francisco
dc.subject.proposal.eng.fl_str_mv 3D maize database
3D reconstruction
LiDAR platform
Plant phenotyping
Point clouds
topic 3D maize database
3D reconstruction
LiDAR platform
Plant phenotyping
Point clouds
description Currently, there are no free databases of 3D point clouds and images for seedling phenotyping. Therefore, this paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research. In total, 362 maize seedlings were recorded using an RGB camera and a SICK LMS4121R-13000 laser scanner with angular resolutions of 45° and 0.5° respectively. The scanned plants are diverse, with seedling captures ranging from less than 10 cm to 40 cm, and ranging from 7 to 24 days after planting in different light conditions in an indoor setting. The point clouds were processed to remove noise and imperfections with a mean absolute precision error of 0.03 cm, synchronized with the images, and time-stamped. The database includes the raw and processed data and manually assigned stem and leaf labels. As an example of a database application, a Random Forest classifier was employed to identify seedling parts based on morphological descriptors, with an accuracy of 89.41%.
publishDate 2022
dc.date.issued.none.fl_str_mv 2022-08-25
dc.date.accessioned.none.fl_str_mv 2023-10-17T21:51:51Z
dc.date.available.none.fl_str_mv 2023-10-17T21:51:51Z
dc.type.none.fl_str_mv Artículo de revista
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dc.identifier.citation.none.fl_str_mv Forero, M.G.; Murcia, H.F.; Méndez, D.; Betancourt-Lozano, J. LiDAR Platform for Acquisition of 3D Plant Phenotyping Database. Plants 2022, 11, 2199. https://doi.org/10.3390/plants11172199
dc.identifier.issn.none.fl_str_mv 22237747
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12313/3851
identifier_str_mv Forero, M.G.; Murcia, H.F.; Méndez, D.; Betancourt-Lozano, J. LiDAR Platform for Acquisition of 3D Plant Phenotyping Database. Plants 2022, 11, 2199. https://doi.org/10.3390/plants11172199
22237747
url https://hdl.handle.net/20.500.12313/3851
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.citationendpage.none.fl_str_mv 15
dc.relation.citationissue.none.fl_str_mv 2199
dc.relation.citationstartpage.none.fl_str_mv 2
dc.relation.citationvolume.none.fl_str_mv 11
dc.relation.ispartofjournal.none.fl_str_mv Plants
dc.relation.references.none.fl_str_mv United Nations Department of Economic and Social Affairs Population Division. Available online: https://n9.cl/vbs5ri (accessed on 6 October 2021).
Li, L.; Zhang, Q.; Huang, D. A Review of Imaging Techniques for Plant Phenotyping. Sensors 2014, 14, 20078–20111
Li, Z.; Guo, R.; Li, M.; Chen, Y.; Li, G. A review of computer vision technologies for plant phenotyping. Comput. Electron. Agric. 2020, 176, 105672
Fahlgren, N.; Gehan, M.A.; Baxter, I. Lights, camera, action: High-throughput plant phenotyping is ready for a close-up. Curr. Opin. Plant Biol. 2015, 24, 93–99
Gao, M.; Yang, F.; Wei, H.; Liu, X. Individual Maize Location and Height Estimation in Field from UAV-Borne LiDAR and RGB Images. Remote Sens. 2022, 14, 2292
Chen, Q.; Gao, T.; Zhu, J.; Wu, F.; Li, X.; Lu, D.; Yu, F. Individual Tree Segmentation and Tree Height Estimation Using Leaf-Off and Leaf-On UAV-LiDAR Data in Dense Deciduous Forests. Remote Sens. 2022, 14, 2787
Gyawali, A.; Aalto, M.; Peuhkurinen, J.; Villikka, M.; Ranta, T. Comparison of Individual Tree Height Estimated from LiDAR and Digital Aerial Photogrammetry in Young Forests. Sustainability 2022, 14, 3720
Wang, Y.; Wen, W.; Wu, S.; Wang, C.; Yu, Z.; Guo, X.; Zhao, C. Maize Plant Phenotyping: Comparing 3D Laser Scanning, Multi-View Stereo Reconstruction, and 3D Digitizing Estimates. Remote Sens. 2018, 11, 63
Zhang, X.; Huang, C.; Wu, D.; Qiao, F.; Li, W.; Duan, L.; Wang, K.; Xiao, Y.; Chen, G.; Liu, Q.; et al. High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth. Plant Physiol. 2017, 173, 1554–1564
Cabrera-Bosquet, L.; Fournier, C.; Brichet, N.; Welcker, C.; Suard, B.; Tardieu, F. High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform. New Phytol. 2016, 212, 269–281
Guo, Q.; Wu, F.; Pang, S.; Zhao, X.; Chen, L.; Liu, J.; Xue, B.; Xu, G.; Li, L.; Jing, H.; et al. Crop 3D—A LiDAR based platform for 3D high-throughput crop phenotyping. Sci. China Life Sci. 2018, 61, 328–339
Young, S.N.; Kayacan, E.; Peschel, J.M. Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum. Precis. Agric. 2019, 20, 697–722
Leotta, M.J.; Vandergon, A.; Taubin, G. Interactive 3D Scanning Without Tracking. In Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2007), Minas Gerais, Brazil, 7–10 October 2007; pp. 205–212
Quan, L.; Wang, J.; Tan, P.; Yuan, L. Image-based modeling by joint segmentation. Int. J. Comput. Vis. 2007, 75, 135–150
Pollefeys, M.; Koch, R.; Vergauwen, M.; Van Gool, L. An automatic method for acquiring 3D models from photographs: Applications to an archaeological site. In Proceedings of the ISPRS International Workshop on Photogrammetric Measurements, Object Modeling and Documentation in Architecture and Industry, Thessaloniki, Greece, 7–9 July 1999
Leiva, F.; Vallenback, P.; Ekblad, T.; Johansson, E.; Chawade, A. Phenocave: An Automated, Standalone, and Affordable Phenotyping System for Controlled Growth Conditions. Plants 2021, 10, 1817
Murcia, H.F.; Tilaguy, S.; Ouazaa, S. Development of a Low-Cost System for 3D Orchard Mapping Integrating UGV and LiDAR. Plants 2021, 10, 2804
Murcia, H.; Sanabria, D.; Méndez, D.; Forero, M.G. A Comparative Study of 3D Plant Modeling Systems Based on Low-Cost 2D LiDAR and Kinect. In Proceedings of the Mexican Conference on Pattern Recognition, Mexico City, Mexico, 23–26 June 2021; Springer: Berlin/Heidelberg, Germany, 2021; pp. 272–281
Brichet, N.; Fournier, C.; Turc, O.; Strauss, O.; Artzet, S.; Pradal, C.; Welcker, C.; Tardieu, F.; Cabrera-Bosquet, L. A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform. Plant Methods 2017, 13, 96
Reiser, D.; Vázquez-Arellano, M.; Paraforos, D.S.; Garrido-Izard, M.; Griepentrog, H.W. Iterative individual plant clustering in maize with assembled 2D LiDAR data. Comput. Ind. 2018, 99, 42–52
Vázquez-Arellano, M.; Reiser, D.; Paraforos, D.S.; Garrido-Izard, M.; Burce, M.E.C.; Griepentrog, H.W. 3-D reconstruction of maize plants using a time-of-flight camera. Comput. Electron. Agric. 2018, 145, 235–247
Vázquez-Arellano, M.; Paraforos, D.S.; Reiser, D.; Garrido-Izard, M.; Griepentrog, H.W. Determination of stem position and height of reconstructed maize plants using a time-of-flight camera. Comput. Electron. Agric. 2018, 154, 276–288
Bao, Y.; Tang, L.; Srinivasan, S.; Schnable, P.S. Field-based architectural traits characterisation of maize plant using time-of-flight 3D imaging. Biosyst. Eng. 2019, 178, 86–101
Qiu, Q.; Sun, N.; Bai, H.; Wang, N.; Fan, Z.; Wang, Y.; Meng, Z.; Li, B.; Cong, Y. Field-Based High-Throughput Phenotyping for Maize Plant Using 3D LiDAR Point Cloud Generated With a “Phenomobile”. Front. Plant Sci. 2019, 10, 554
McCormick, R.F.; Truong, S.K.; Mullet, J.E. 3D sorghum reconstructions from depth images identify QTL regulating shoot architecture. Plant Physiol. 2016, 172, 823–834
Paulus, S.; Schumann, H.; Kuhlmann, H.; Léon, J. High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants. Biosyst. Eng. 2014, 121, 1–11
Thapa, S.; Zhu, F.; Walia, H.; Yu, H.; Ge, Y. A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum. Sensors 2018, 18, 1187
Lehning, M.; SICK. sick_scan. Available online: https://github.com/SICKAG/sick_scan (accessed on 6 October 2021)
Pitzer, B.; Toris, R. usb_cam. Available online: https://github.com/ros-drivers/usb_cam (accessed on 6 October 2021).
Balta, H.; Velagic, J.; Bosschaerts, W.; De Cubber, G.; Siciliano, B. Fast statistical outlier removal based method for large 3D point clouds of outdoor environments. IFAC-PapersOnLine 2018, 51, 348–353
Gelard, W.; Devy, M.; Herbulot, A.; Burger, P. Model-based segmentation of 3D point clouds for phenotyping sunflower plants. In Proceedings of the 12 International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Porto, Portugal, 27 February 2017
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spelling Comeche, José Manuel98ed7133-bd92-49e9-9393-ed820587227f600Murcia, Harold F.dbc160fc-bf06-453b-a8b0-2d93dbce3c97-1Méndez, Dehyrofd840f52-199a-48f8-ab04-7c11fed9e015-1Martínez Pérez, Juan Franciscoe1337f24-7a73-4d17-ace7-092e92eed3a16002023-10-17T21:51:51Z2023-10-17T21:51:51Z2022-08-25Currently, there are no free databases of 3D point clouds and images for seedling phenotyping. Therefore, this paper describes a platform for seedling scanning using 3D Lidar with which a database was acquired for use in plant phenotyping research. In total, 362 maize seedlings were recorded using an RGB camera and a SICK LMS4121R-13000 laser scanner with angular resolutions of 45° and 0.5° respectively. The scanned plants are diverse, with seedling captures ranging from less than 10 cm to 40 cm, and ranging from 7 to 24 days after planting in different light conditions in an indoor setting. The point clouds were processed to remove noise and imperfections with a mean absolute precision error of 0.03 cm, synchronized with the images, and time-stamped. The database includes the raw and processed data and manually assigned stem and leaf labels. As an example of a database application, a Random Forest classifier was employed to identify seedling parts based on morphological descriptors, with an accuracy of 89.41%.application/pdfForero, M.G.; Murcia, H.F.; Méndez, D.; Betancourt-Lozano, J. LiDAR Platform for Acquisition of 3D Plant Phenotyping Database. Plants 2022, 11, 2199. https://doi.org/10.3390/plants1117219922237747https://hdl.handle.net/20.500.12313/3851engSuiza152199211PlantsUnited Nations Department of Economic and Social Affairs Population Division. Available online: https://n9.cl/vbs5ri (accessed on 6 October 2021).Li, L.; Zhang, Q.; Huang, D. A Review of Imaging Techniques for Plant Phenotyping. Sensors 2014, 14, 20078–20111Li, Z.; Guo, R.; Li, M.; Chen, Y.; Li, G. A review of computer vision technologies for plant phenotyping. Comput. Electron. Agric. 2020, 176, 105672Fahlgren, N.; Gehan, M.A.; Baxter, I. Lights, camera, action: High-throughput plant phenotyping is ready for a close-up. Curr. Opin. Plant Biol. 2015, 24, 93–99Gao, M.; Yang, F.; Wei, H.; Liu, X. Individual Maize Location and Height Estimation in Field from UAV-Borne LiDAR and RGB Images. Remote Sens. 2022, 14, 2292Chen, Q.; Gao, T.; Zhu, J.; Wu, F.; Li, X.; Lu, D.; Yu, F. Individual Tree Segmentation and Tree Height Estimation Using Leaf-Off and Leaf-On UAV-LiDAR Data in Dense Deciduous Forests. Remote Sens. 2022, 14, 2787Gyawali, A.; Aalto, M.; Peuhkurinen, J.; Villikka, M.; Ranta, T. Comparison of Individual Tree Height Estimated from LiDAR and Digital Aerial Photogrammetry in Young Forests. Sustainability 2022, 14, 3720Wang, Y.; Wen, W.; Wu, S.; Wang, C.; Yu, Z.; Guo, X.; Zhao, C. Maize Plant Phenotyping: Comparing 3D Laser Scanning, Multi-View Stereo Reconstruction, and 3D Digitizing Estimates. Remote Sens. 2018, 11, 63Zhang, X.; Huang, C.; Wu, D.; Qiao, F.; Li, W.; Duan, L.; Wang, K.; Xiao, Y.; Chen, G.; Liu, Q.; et al. High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth. Plant Physiol. 2017, 173, 1554–1564Cabrera-Bosquet, L.; Fournier, C.; Brichet, N.; Welcker, C.; Suard, B.; Tardieu, F. High-throughput estimation of incident light, light interception and radiation-use efficiency of thousands of plants in a phenotyping platform. New Phytol. 2016, 212, 269–281Guo, Q.; Wu, F.; Pang, S.; Zhao, X.; Chen, L.; Liu, J.; Xue, B.; Xu, G.; Li, L.; Jing, H.; et al. Crop 3D—A LiDAR based platform for 3D high-throughput crop phenotyping. Sci. China Life Sci. 2018, 61, 328–339Young, S.N.; Kayacan, E.; Peschel, J.M. Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum. Precis. Agric. 2019, 20, 697–722Leotta, M.J.; Vandergon, A.; Taubin, G. Interactive 3D Scanning Without Tracking. In Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2007), Minas Gerais, Brazil, 7–10 October 2007; pp. 205–212Quan, L.; Wang, J.; Tan, P.; Yuan, L. Image-based modeling by joint segmentation. Int. J. Comput. Vis. 2007, 75, 135–150Pollefeys, M.; Koch, R.; Vergauwen, M.; Van Gool, L. An automatic method for acquiring 3D models from photographs: Applications to an archaeological site. In Proceedings of the ISPRS International Workshop on Photogrammetric Measurements, Object Modeling and Documentation in Architecture and Industry, Thessaloniki, Greece, 7–9 July 1999Leiva, F.; Vallenback, P.; Ekblad, T.; Johansson, E.; Chawade, A. Phenocave: An Automated, Standalone, and Affordable Phenotyping System for Controlled Growth Conditions. Plants 2021, 10, 1817Murcia, H.F.; Tilaguy, S.; Ouazaa, S. Development of a Low-Cost System for 3D Orchard Mapping Integrating UGV and LiDAR. Plants 2021, 10, 2804Murcia, H.; Sanabria, D.; Méndez, D.; Forero, M.G. A Comparative Study of 3D Plant Modeling Systems Based on Low-Cost 2D LiDAR and Kinect. In Proceedings of the Mexican Conference on Pattern Recognition, Mexico City, Mexico, 23–26 June 2021; Springer: Berlin/Heidelberg, Germany, 2021; pp. 272–281Brichet, N.; Fournier, C.; Turc, O.; Strauss, O.; Artzet, S.; Pradal, C.; Welcker, C.; Tardieu, F.; Cabrera-Bosquet, L. A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform. Plant Methods 2017, 13, 96Reiser, D.; Vázquez-Arellano, M.; Paraforos, D.S.; Garrido-Izard, M.; Griepentrog, H.W. Iterative individual plant clustering in maize with assembled 2D LiDAR data. Comput. Ind. 2018, 99, 42–52Vázquez-Arellano, M.; Reiser, D.; Paraforos, D.S.; Garrido-Izard, M.; Burce, M.E.C.; Griepentrog, H.W. 3-D reconstruction of maize plants using a time-of-flight camera. Comput. Electron. Agric. 2018, 145, 235–247Vázquez-Arellano, M.; Paraforos, D.S.; Reiser, D.; Garrido-Izard, M.; Griepentrog, H.W. Determination of stem position and height of reconstructed maize plants using a time-of-flight camera. Comput. Electron. Agric. 2018, 154, 276–288Bao, Y.; Tang, L.; Srinivasan, S.; Schnable, P.S. Field-based architectural traits characterisation of maize plant using time-of-flight 3D imaging. Biosyst. Eng. 2019, 178, 86–101Qiu, Q.; Sun, N.; Bai, H.; Wang, N.; Fan, Z.; Wang, Y.; Meng, Z.; Li, B.; Cong, Y. Field-Based High-Throughput Phenotyping for Maize Plant Using 3D LiDAR Point Cloud Generated With a “Phenomobile”. Front. Plant Sci. 2019, 10, 554McCormick, R.F.; Truong, S.K.; Mullet, J.E. 3D sorghum reconstructions from depth images identify QTL regulating shoot architecture. Plant Physiol. 2016, 172, 823–834Paulus, S.; Schumann, H.; Kuhlmann, H.; Léon, J. High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants. Biosyst. Eng. 2014, 121, 1–11Thapa, S.; Zhu, F.; Walia, H.; Yu, H.; Ge, Y. A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum. Sensors 2018, 18, 1187Lehning, M.; SICK. sick_scan. Available online: https://github.com/SICKAG/sick_scan (accessed on 6 October 2021)Pitzer, B.; Toris, R. usb_cam. Available online: https://github.com/ros-drivers/usb_cam (accessed on 6 October 2021).Balta, H.; Velagic, J.; Bosschaerts, W.; De Cubber, G.; Siciliano, B. Fast statistical outlier removal based method for large 3D point clouds of outdoor environments. IFAC-PapersOnLine 2018, 51, 348–353Gelard, W.; Devy, M.; Herbulot, A.; Burger, P. Model-based segmentation of 3D point clouds for phenotyping sunflower plants. In Proceedings of the 12 International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Porto, Portugal, 27 February 2017This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Atribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/https://www.mdpi.com/2223-7747/11/17/21993D maize database3D reconstructionLiDAR platformPlant phenotypingPoint cloudsLiDAR platform for acquisition of 3d plant phenotyping databaseArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionPublicationTEXTLiDAR Platform for Acquisition of 3D Plant Phenotyping Database - plants-11-02199-v2.pdf.txtLiDAR Platform for Acquisition of 3D Plant Phenotyping Database - plants-11-02199-v2.pdf.txtExtracted texttext/plain4347https://repositorio.unibague.edu.co/bitstreams/bb6e9fa9-8a64-4fae-905e-94fafb5315a4/download9b10939a82265fc7ab4fb851fcc136c5MD53THUMBNAILLiDAR Platform for Acquisition of 3D Plant Phenotyping Database - plants-11-02199-v2.pdf.jpgLiDAR Platform for Acquisition of 3D Plant Phenotyping Database - plants-11-02199-v2.pdf.jpgGenerated Thumbnailimage/jpeg10373https://repositorio.unibague.edu.co/bitstreams/49a16196-dff8-4f4e-a301-7970b64ae6cf/download2de57538ae885a2456d508ea29f550cbMD54LICENSElicense.txtlicense.txttext/plain; charset=utf-8134https://repositorio.unibague.edu.co/bitstreams/c316a6cd-61ec-4ed3-869d-f6fd96a9c720/download2fa3e590786b9c0f3ceba1b9656b7ac3MD52ORIGINALLiDAR Platform for Acquisition of 3D Plant Phenotyping Database - plants-11-02199-v2.pdfLiDAR Platform for Acquisition of 3D Plant Phenotyping Database - plants-11-02199-v2.pdfapplication/pdf109231https://repositorio.unibague.edu.co/bitstreams/49bfd78e-f5c3-41d1-b19f-1dd49e95ed19/download6de3b24d5caadc3530da1c7752d176a4MD5120.500.12313/3851oai:repositorio.unibague.edu.co:20.500.12313/38512023-10-18 03:00:42.687https://creativecommons.org/licenses/by-nc-nd/4.0/This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).https://repositorio.unibague.edu.coRepositorio Institucional Universidad de Ibaguébdigital@metabiblioteca.comQ3JlYXRpdmUgQ29tbW9ucyBBdHRyaWJ1dGlvbi1Ob25Db21tZXJjaWFsLU5vRGVyaXZhdGl2ZXMgNC4wIEludGVybmF0aW9uYWwgTGljZW5zZQ0KaHR0cHM6Ly9jcmVhdGl2ZWNvbW1vbnMub3JnL2xpY2Vuc2VzL2J5LW5jLW5kLzQuMC8=

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