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:
Forero, Manuel G.
Murcia, Harold F
Méndez, Dehyro
Betancourt-Lozano, Juan
Tipo de recurso:
Article of investigation
Fecha de publicación:
2022
Institución:
Universidad de Ibagué
Repositorio:
Repositorio Universidad de Ibagué
Idioma:
eng
OAI Identifier:
oai:repositorio.unibague.edu.co:20.500.12313/5503
Acceso en línea:
https://hdl.handle.net/20.500.12313/5503
Palabra clave:
Fenotipado de plantas
Plataforma LiDAR
3D maize database
3D reconstruction
LiDAR platform
Plant phenotyping
Point clouds
Rights
openAccess
License
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
id UNIBAGUE2_65c62154ded2cf649a673d473dd9b840
oai_identifier_str oai:repositorio.unibague.edu.co:20.500.12313/5503
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
Fenotipado de plantas
Plataforma LiDAR
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 Forero, Manuel G.
Murcia, Harold F
Méndez, Dehyro
Betancourt-Lozano, Juan
dc.contributor.author.none.fl_str_mv Forero, Manuel G.
Murcia, Harold F
Méndez, Dehyro
Betancourt-Lozano, Juan
dc.subject.armarc.none.fl_str_mv Fenotipado de plantas
Plataforma LiDAR
topic Fenotipado de plantas
Plataforma LiDAR
3D maize database
3D reconstruction
LiDAR platform
Plant phenotyping
Point clouds
dc.subject.proposal.eng.fl_str_mv 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 2022-09
dc.date.accessioned.none.fl_str_mv 2025-08-20T21:40:14Z
dc.date.available.none.fl_str_mv 2025-08-20T21:40:14Z
dc.type.none.fl_str_mv Artículo de revista
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dc.identifier.citation.none.fl_str_mv Forero, M., Murcia, H., Méndez, D. y Betancourt-Lozano, J. (2022). LiDAR Platform for Acquisition of 3D Plant Phenotyping Database. Plants, 11(17), 2199. DOI: 10.3390/plants11172199
dc.identifier.doi.none.fl_str_mv 10.3390/plants11172199
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12313/5503
identifier_str_mv Forero, M., Murcia, H., Méndez, D. y Betancourt-Lozano, J. (2022). LiDAR Platform for Acquisition of 3D Plant Phenotyping Database. Plants, 11(17), 2199. DOI: 10.3390/plants11172199
10.3390/plants11172199
url https://hdl.handle.net/20.500.12313/5503
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.citationissue.none.fl_str_mv 17
dc.relation.citationstartpage.none.fl_str_mv 2199
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 Forero, Manuel G.221ba9eb-1b06-4908-aac9-b50cd974a391-1Murcia, Harold Fdbc160fc-bf06-453b-a8b0-2d93dbce3c97-1Méndez, Dehyrofd840f52-199a-48f8-ab04-7c11fed9e015-1Betancourt-Lozano, Juan7b99dae6-1ce5-4c76-a725-f93d09eab2b4-12025-08-20T21:40:14Z2025-08-20T21:40:14Z2022-09Currently, 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., Murcia, H., Méndez, D. y Betancourt-Lozano, J. (2022). LiDAR Platform for Acquisition of 3D Plant Phenotyping Database. Plants, 11(17), 2199. DOI: 10.3390/plants1117219910.3390/plants11172199https://hdl.handle.net/20.500.12313/5503engMDPISwiza17219911PlantsUnited 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, 235–247Vázquez-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, 276–288.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.© 2022 by the authors. 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