Advertisement

Science China Life Sciences

, Volume 61, Issue 3, pp 328–339 | Cite as

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

  • Qinghua Guo
  • Fangfang Wu
  • Shuxin Pang
  • Xiaoqian Zhao
  • Linhai Chen
  • Jin Liu
  • Baolin Xue
  • Guangcai Xu
  • Le Li
  • Haichun Jing
  • Chengcai Chu
Research Paper

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.

Keywords

crop breeding phenotypic traits data fusion LiDAR high-throughput integrated platform 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

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).

References

  1. 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.CrossRefGoogle Scholar
  2. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 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.CrossRefGoogle Scholar
  4. Araus, J.L., and Cairns, J.E. (2014). Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19, 52–61.CrossRefPubMedGoogle Scholar
  5. Bongiovanni, R., and Lowenberg-Deboer, J. (2004). Precision agriculture and sustainability. Precis Agric 5, 359–387.CrossRefGoogle Scholar
  6. 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
  7. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. 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.CrossRefGoogle Scholar
  10. 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.CrossRefPubMedGoogle Scholar
  11. Fiorani, F., and Schurr, U. (2013). Future scenarios for plant phenotyping. Annu Rev Plant Biol 64, 267–291.CrossRefPubMedGoogle Scholar
  12. 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
  13. Furbank, R.T., and Tester, M. (2011). Phenomics-technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16, 635–644.CrossRefPubMedGoogle Scholar
  14. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 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
  16. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. Houle, D., Govindaraju, D.R., and Omholt, S. (2010). Phenomics: the next challenge. Nat Rev Genet 11, 855–866.CrossRefPubMedGoogle Scholar
  20. 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.CrossRefPubMedGoogle Scholar
  21. Lefsky, M.A., Cohen, W.B., Parker, G.G., and Harding, D.J. (2002). Lidar remote sensing for ecosystem studies. BioScience 52, 19–30.CrossRefGoogle Scholar
  22. 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
  23. 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
  24. Li, L., Zhang, Q., and Huang, D. (2014). A review of imaging techniques for plant phenotyping. Sensors 14, 20078–20111.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 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.CrossRefGoogle Scholar
  26. 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.CrossRefPubMedGoogle Scholar
  27. 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.CrossRefGoogle Scholar
  28. 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.CrossRefPubMedGoogle Scholar
  29. 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.CrossRefGoogle Scholar
  30. 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.CrossRefPubMedGoogle Scholar
  31. Mulla, D.J. (2013). Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosyst Eng 114, 358–371.CrossRefGoogle Scholar
  32. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 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
  34. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  36. Peleman, J.D., and van der Voort, J.R. (2003). Breeding by design. Trends Plant Sci 8, 330–334.CrossRefPubMedGoogle Scholar
  37. 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.CrossRefGoogle Scholar
  38. 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.CrossRefGoogle Scholar
  39. 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
  40. 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.CrossRefGoogle Scholar
  41. 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.CrossRefGoogle Scholar
  42. 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.CrossRefGoogle Scholar
  43. 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
  44. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 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.CrossRefGoogle Scholar
  46. Wang, A. (2002). Extraction of growth parameters of winter wheat based on Terrestrial LiDAR data (in Chinese). Master Dissertation. (Nanjing: Nanjing University).Google Scholar
  47. 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.CrossRefGoogle Scholar
  48. Wan, J. (2006). Perspectives of molecular design breeding in crops (in Chinese). Acta Agron Sin 32, 455–462.Google Scholar
  49. 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.CrossRefGoogle Scholar
  50. 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
  51. 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
  52. 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.CrossRefPubMedPubMedCentralGoogle Scholar
  53. Yu, G., and Fang, X. (2009). Concept of phenomics and its development in plant science (in Chinese). Mol Plant Breed 7, 639–645.Google Scholar
  54. Zhang, Y.M. (2006). Advances on methods for mapping QTL in plant. Chin Sci Bull 51, 2809–2818.CrossRefGoogle Scholar
  55. 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

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Qinghua Guo
    • 1
  • Fangfang Wu
    • 1
    • 2
  • Shuxin Pang
    • 1
  • Xiaoqian Zhao
    • 1
    • 2
  • Linhai Chen
    • 1
    • 2
  • Jin Liu
    • 1
  • Baolin Xue
    • 1
  • Guangcai Xu
    • 1
  • Le Li
    • 3
  • Haichun Jing
    • 1
  • Chengcai Chu
    • 4
  1. 1.State Key Laboratory of Vegetation and Environmental Change, Institute of BotanyChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.College of Life Science and TechnologyBeijing Normal UniversityBeijingChina
  4. 4.State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijingChina

Personalised recommendations