Skip to main content
SpringerLink
Log in

Crop 3D—a LiDAR based platform for 3D high-throughput crop phenotyping

  • Research Paper
  • Published:
Science China Life Sciences Aims and scope Submit manuscript

Abstract

With the growing population and the reducing arable land, breeding has been considered as an effective way to solve the food crisis. As an important part in breeding, high-throughput phenotyping can accelerate the breeding process effectively. Light detection and ranging (LiDAR) is an active remote sensing technology that is capable of acquiring three-dimensional (3D) data accurately, and has a great potential in crop phenotyping. Given that crop phenotyping based on LiDAR technology is not common in China, we developed a high-throughput crop phenotyping platform, named Crop 3D, which integrated LiDAR sensor, high-resolution camera, thermal camera and hyperspectral imager. Compared with traditional crop phenotyping techniques, Crop 3D can acquire multi-source phenotypic data in the whole crop growing period and extract plant height, plant width, leaf length, leaf width, leaf area, leaf inclination angle and other parameters for plant biology and genomics analysis. In this paper, we described the designs, functions and testing results of the Crop 3D platform, and briefly discussed the potential applications and future development of the platform in phenotyping. We concluded that platforms integrating LiDAR and traditional remote sensing techniques might be the future trend of crop high-throughput phenotyping.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Andújar, D., Escolà, A., Rosell-Polo, J.R., Fernández-Quintanilla, C., and Dorado, J. (2013a). Potential of a terrestrial LiDAR-based system to characterise weed vegetation in maize crops. Comp Electron Agric 92, 11–15.

    Article  Google Scholar 

  • Andújar, D., Rueda-Ayala, V., Moreno, H., Rosell-Polo, J.R., Escolá, A., Valero, C., Gerhards, R., Fernández-Quintanilla, C., Dorado, J., and Griepentrog, H.W. (2013b). Discriminating crop, weeds and soil surface with a terrestrial LIDAR sensor. Sensors 13, 14662–14675.

    Article  PubMed  PubMed Central  Google Scholar 

  • Andrade-Sanchez, P., Gore, M.A., Heun, J.T., Thorp, K.R., Carmo-Silva, A.E., French, A.N., Salvucci, M.E., and White, J.W. (2014). Development and evaluation of a field-based high-throughput phenotyping platform. Funct Plant Biol 41, 68–79.

    Article  Google Scholar 

  • Araus, J.L., and Cairns, J.E. (2014). Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19, 52–61.

    Article  CAS  PubMed  Google Scholar 

  • Bongiovanni, R., and Lowenberg-Deboer, J. (2004). Precision agriculture and sustainability. Precis Agric 5, 359–387.

    Article  Google Scholar 

  • Bruinsma, J. (2009). The Resource Outlook to 2050: by how much do land, water and crop yields need to increase by 2050? FAO Expert Meeting on How to Feed the World in 2050, Rome, Italy. pp. 1–33.

    Google Scholar 

  • Busemeyer, L., Mentrup, D., Möller, K., Wunder, E., Alheit, K., Hahn, V., Maurer, H.P., Reif, J.C., Würschum, T., Müller, J., Rahe, F., and Ruckelshausen, A. (2013). Breedvision—A multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors 13, 2830–2847.

    Article  PubMed  PubMed Central  Google Scholar 

  • Chéné, Y., Rousseau, D., Lucidarme, P., Bertheloot, J., Caffier, V., Morel, P., Belin, É., and Chapeau-Blondeau, F. (2012). On the use of depth camera for 3D phenotyping of entire plants. Comp Electron Agric 82, 122–127.

    Article  Google Scholar 

  • Deery, D., Jimenez-Berni, J., Jones, H., Sirault, X., and Furbank, R. (2014). Proximal remote sensing buggies and potential applications for fieldbased phenotyping. Agronomy 4, 349–379.

    Article  Google Scholar 

  • Dhondt, S., Wuyts, N., and Inzé, D. (2013). Cell to whole-plant phenotyping: the best is yet to come. Trends Plant Sci 18, 428–439.

    Article  CAS  PubMed  Google Scholar 

  • Fiorani, F., and Schurr, U. (2013). Future scenarios for plant phenotyping. Annu Rev Plant Biol 64, 267–291.

    Article  CAS  PubMed  Google Scholar 

  • Fujino, M., Endo, R., and Omasa K. (2002). Nondestructive instrumentation of water-stressed cucumber leaves: comparison among changes in spectral reflectance, stomatal conductance, psii yield and shape. Agri Inform Res 11, 161–170.

    Google Scholar 

  • Furbank, R.T., and Tester, M. (2011). Phenomics-technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16, 635–644.

    Article  CAS  PubMed  Google Scholar 

  • Hartmann, A., Czauderna, T., Hoffmann, R., Stein, N., and Schreiber, F. (2011). HTPheno: an image analysis pipeline for high-throughput plant phenotyping. BMC Bioinformatics 12, 148.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hoffmeister, D., Curdt, C., Tilly, N., and Bendig, J. (2010). 3D terrestrial laser scanning for field crop modelling. In: Workshop on Remote Sensing Methods forChange Detection and Process Modelling, V. Lenz-Wiedemann, G. Bareth, eds. pp.17–22.

    Google Scholar 

  • Hosoi, F., Nakabayashi, K., and Omasa, K. (2011). 3-D modeling of tomato canopies using a high-resolution portable scanning lidar for extracting structural information. Sensors 11, 2166–2174.

    Article  PubMed  PubMed Central  Google Scholar 

  • Hosoi, F., Nakai, Y., and Omasa, K. (2009). Estimating the leaf inclination angle distribution of the wheat canopy using a portable scanning lidar. J Agric Meteorol 65, 297–302.

    Article  Google Scholar 

  • Hosoi, F., and Omasa, K. (2009). Estimating vertical plant area density profile and growth parameters of a wheat canopy at different growth stages using three-dimensional portable lidar imaging. ISPRS J Photogramm Remote Sens 64, 151–158.

    Article  Google Scholar 

  • Houle, D., Govindaraju, D.R., and Omholt, S. (2010). Phenomics: the next challenge. Nat Rev Genet 11, 855–866.

    Article  CAS  PubMed  Google Scholar 

  • Hu, W., and Chen, J. (2015). Whole-genome sequencing opens a new era for molecular breeding of grass carp (Ctenopharyngodon idellus). Sci China Life Sci 58, 619–620.

    Article  PubMed  Google Scholar 

  • Lefsky, M.A., Cohen, W.B., Parker, G.G., and Harding, D.J. (2002). Lidar remote sensing for ecosystem studies. BioScience 52, 19–30.

    Article  Google Scholar 

  • Liang, J., and Yang, J. (2007). Study on image process application in maize plant type (in Chinese). Acta Agron Sin 15, 146–148.

    Google Scholar 

  • Liang, Y., and Wang, Y. (2006). The genes controlling rice architecture and its application in breeding (in Chinese). Chin Bull of Life Sci 28, 1156–1167.

    Google Scholar 

  • Li, L., Zhang, Q., and Huang, D. (2014). A review of imaging techniques for plant phenotyping. Sensors 14, 20078–20111.

    Article  PubMed  PubMed Central  Google Scholar 

  • Li, W., Guo, Q., Jakubowski, M.K., and Kelly, M. (2012). A new method for segmenting individual trees from the lidar point cloud. Photogramm Eng Rem Sens 78, 75–84.

    Article  Google Scholar 

  • Li, X., Qian, Q., Fu, Z., Wang, Y., Xiong, G., Zeng, D., Wang, X., Liu, X., Teng, S., Hiroshi, F., Yuan, M., Luo, D., Han, B., and Li, J. (2003). Control of tillering in rice. Nature 422, 618–621.

    Article  CAS  PubMed  Google Scholar 

  • Luo, P., Ren, Z., Wu, X., Zhang, H., Zhang, H., and Feng, J. (2006). Structural and biochemical mechanism responsible for the stay-green phenotype in common wheat. Chin Sci Bull 51, 2595–2603.

    Article  CAS  Google Scholar 

  • Möller, M., Alchanatis, V., Cohen, Y., Meron, M., Tsipris, J., Naor, A., Ostrovsky, V., Sprintsin, M., and Cohen, S. (2006). Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. J Exp Bot 58, 827–838.

    Article  PubMed  Google Scholar 

  • Montes, J.M., Technow, F., Dhillon, B.S., Mauch, F., and Melchinger, A.E. (2011). High-throughput non-destructive biomass determination during early plant development in maize under field conditions. Field Crops Res 121, 268–273.

    Article  Google Scholar 

  • Montes, J.M., Melchinger, A.E., and Reif, J.C. (2007). Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci 12, 433–436.

    Article  CAS  PubMed  Google Scholar 

  • Mulla, D.J. (2013). Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosyst Eng 114, 358–371.

    Article  Google Scholar 

  • Paproki, A., Sirault, X., Berry, S., Furbank, R., and Fripp, J. (2012). A novel mesh processing based technique for 3D plant analysis. BMC Plant Biol 12, 63.

    Article  PubMed  PubMed Central  Google Scholar 

  • Pask, A., Pietragalla, J., Mullan, D., and Reynolds, M. (2012). Physiological Breeding II: A Field Guide to Wheat Phenotyping. (EI Batan: CIMMYT), pp. 126–127.

    Google Scholar 

  • Paulus, S., Behmann, J., Mahlein, A.K., Plümer, L., and Kuhlmann, H. (2014). Low-cost 3D systems: suitable tools for plant phenotyping. Sensors 14, 3001–3018.

    Article  PubMed  PubMed Central  Google Scholar 

  • Paulus, S., Dupuis, J., Mahlein, A.K., and Kuhlmann, H. (2013). Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping. BMC Bioinformatics 14, 238.

    Article  PubMed  PubMed Central  Google Scholar 

  • Peleman, J.D., and van der Voort, J.R. (2003). Breeding by design. Trends Plant Sci 8, 330–334.

    Article  CAS  PubMed  Google Scholar 

  • Quan, L., Tan, P., Zeng, G., Yuan, L., Wang, J., and Kang, S.B. (2006). Image-based plant modeling. ACM Trans Graph 25, 599–604.

    Article  Google Scholar 

  • Guo, Q.H., Liu, J., Tao, S.L., Xue, B.L., Li, L., Xu, G.C., Li, W.K., Wu, F.F., Li, Y.M., Chen, L.H., and Pang, S.X. (2014). Perspectives and prospects of LiDAR in forest ecosystem monitoring and modeling (in Chinese). Chin Sci Bull (Chin Ver) 59, 459–478.

    Article  Google Scholar 

  • Reuzeau, C., Pen, J., Frankard, V., de Wolf, J., Peerbolte, R., Broekaert, W., and van Camp, W. (2010). Traitmill: a discovery engine for identifying yield-enhancement genes in cereals. PGT 1, 753–759.

    Google Scholar 

  • Rovira-Más, F., Zhang, Q., and Reid, J.F. (2008). Stereo vision three-dimensional terrain maps for precision agriculture. Comp Electron Agric 60, 133–143.

    Article  Google Scholar 

  • Rundquist, D., Gitelson, A., Leavitt, B., Zygielbaum, A., Perk, R., and Keydan, G. (2014). Elements of an integrated phenotyping system for monitoring crop status at canopy level. Agronomy 4, 108–123.

    Article  Google Scholar 

  • Saeys, W., Lenaerts, B., Craessaerts, G., and De Baerdemaeker, J. (2009). Estimation of the crop density of small grains using LiDAR sensors. Biosyst Eng 102, 22–30.

    Article  Google Scholar 

  • Sirault, X.R.R., Fripp, J., Paproki, A., Kuffner, P., Nguyen, C., Li, R., Daily, H., Guo, J., and Furbank, R. (2015). PlantscanTM: a three-dimensional phenotyping platform for capturing the structural dynamic of plant development and growth. In: Proceedings of the 7th International Conference on Functional-Structural Plant Models. (Saariselka, Finland), pp. 45–48.

    Google Scholar 

  • Sritarapipat, T., Rakwatin, P., and Kasetkasem, T. (2014). Automatic rice crop height measurement using a field server and digital image processing. Sensors 14, 900–926.

    Article  PubMed  PubMed Central  Google Scholar 

  • Tilly, N., Hoffmeister, D., Cao, Q., Huang, S., Lenz-Wiedemann, V., Miao, Y., and Bareth, G. (2014). Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice. J Appl Remote Sens 8, 083671.

    Article  Google Scholar 

  • Wang, A. (2002). Extraction of growth parameters of winter wheat based on Terrestrial LiDAR data (in Chinese). Master Dissertation. (Nanjing: Nanjing University).

    Google Scholar 

  • Wang, H., Zhang, W., Zhou, G., Yan, G., and Clinton, N. (2009). Image-based 3D corn reconstruction for retrieval of geometrical structural parameters. Int J Remote Sens 30, 5505–5513.

    Article  Google Scholar 

  • Wan, J. (2006). Perspectives of molecular design breeding in crops (in Chinese). Acta Agron Sin 32, 455–462.

    CAS  Google Scholar 

  • White, J.W., Andrade-Sanchez, P., Gore, M.A., Bronson, K.F., Coffelt, T.A., Conley, M.M., Feldmann, K.A., French, A.N., Heun, J.T., Hunsaker, D.J., Jenks, M.A., Kimball, B.A., Roth, R.L., Strand, R.J., Thorp, K.R., Wall, G.W., and Wang, G. (2012). Field-based phenomics for plant genetics research. Field Crops Res 133, 101–112.

    Article  Google Scholar 

  • Wu, W., Hong, T., Wang, X., Peng, W., Li, Z, and Zhang, W. (2007). Advance in ground-based LAI measurement methods (in Chinese). J Huazhong Agri Univ 26, 270–275.

    Google Scholar 

  • Xu, X., Guo, N., Ge, Q., and Guo, X. (2006). Application of technology for computer vision in plants shape measurement (in Chinese). Comput Eng Desig 27, 1134–1136.

    Google Scholar 

  • Yang, W., Guo, Z., Huang, C., Duan, L., Chen, G., Jiang, N., Fang, W., Feng, H., Xie, W., Lian, X., Wang, G., Luo, Q., Zhang, Q., Liu, Q., and Xiong, L. (2014). Combining high-throughput phenotyping and genomewide association studies to reveal natural genetic variation in rice. Nat Commun 5, 5087.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yu, G., and Fang, X. (2009). Concept of phenomics and its development in plant science (in Chinese). Mol Plant Breed 7, 639–645.

    CAS  Google Scholar 

  • Zhang, Y.M. (2006). Advances on methods for mapping QTL in plant. Chin Sci Bull 51, 2809–2818.

    Article  CAS  Google Scholar 

  • Zhao C., Lu S., Guo X., Du J., Wen W., and Miao T. (2015). Advances in research of digital plant: 3D digitization of plant morphological structure (in Chinese). Sci Agr Sin 48, 3415–3428.

    Google Scholar 

Download references

Acknowledgments

This work was supported by the Strategic Program of Molecular Module-Based Designer Breeding Systems (XDA08040107), and the Instrument Developing Project of the Chinese Academy of Sciences (2014129).

Author information

Authors and Affiliations

  1. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China

    Qinghua Guo, Fangfang Wu, Shuxin Pang, Xiaoqian Zhao, Linhai Chen, Jin Liu, Baolin Xue, Guangcai Xu & Haichun Jing

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Fangfang Wu, Xiaoqian Zhao & Linhai Chen

  3. College of Life Science and Technology, Beijing Normal University, Beijing, 100875, China

    Le Li

  4. State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China

    Chengcai Chu

Authors
  1. Qinghua Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fangfang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuxin Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaoqian Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Linhai Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Baolin Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Guangcai Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Le Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Haichun Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Chengcai Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qinghua Guo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, Q., Wu, F., Pang, S. et al. Crop 3D—a LiDAR based platform for 3D high-throughput crop phenotyping. Sci. China Life Sci. 61, 328–339 (2018). https://doi.org/10.1007/s11427-017-9056-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11427-017-9056-0

Keywords

Search

Navigation