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Design Considerations for In-Field Measurement of Plant Architecture Traits Using Ground-Based Platforms

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High-Throughput Plant Phenotyping

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2539))

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Abstract

This work provides a high-level overview of system design considerations for measuring plant architecture traits in row crops using ground-based, mobile platforms. High-throughput phenotyping technologies are commonly deployed in isolated growth chambers or greenhouses; however, there is a need for field-based systems to measure large quantities of plants exposed to natural climates throughout a growing season. High-throughput methods using ground-based mobile systems collect valuable phenotypic information at higher temporal resolutions compared to manual methods (e.g., handheld calipers and measuring sticks). Additionally, the close proximity to plants when using ground-based systems compared to aerial platforms enables plant phenotyping at the organ level. While there is no single best platform for obtaining ground-based plant measurements across crop varieties with different planting configurations, there are a wide range of off-the-shelf systems and sensors that can be integrated to accommodate varying row widths, plant spacing, plant heights, and plot sizes, in addition to emerging commercially available platforms. This chapter will provide an overview of sensor types suitable for phenotyping plant size and shape, as well as provide guidance for deployment with ground-based systems, including push carts or buggies, modified tractors, and robotic platforms.

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References

  1. Batz J, Méndez-Dorado AM, Thomasson AJ (2016) Imaging for high-throughput phenotyping in energy sorghum. J Imaging 2(1). https://doi.org/10.3390/jimaging2010004

  2. Fiorani F, Tuberosa R (2013) Future scenarios for plant phenotyping. Ann Rev Plant Biol 64:267–291

    Article  CAS  Google Scholar 

  3. Furbank R (2009) Plant phenomics: from gene to form and function. Funct Plant Biol 36:10–11

    Article  Google Scholar 

  4. Sankaran S, Khot LR, Espinoza CZ et al (2015) Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review. Eur J Agron 70:112–123

    Article  Google Scholar 

  5. White JW, Andrade-Sanchez P, Gore MA et al (2012) Field-based phenomics for plant genetics research. Field Crops Res 133:101–112

    Article  Google Scholar 

  6. Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19(1):52–61

    Article  CAS  Google Scholar 

  7. Wight JP, Hons FM, Storlien JO et al (2012) Management effects on bioenergy sorghum growth, yield and nutrient uptake. Biomass Bioeng 46:593–604

    Article  CAS  Google Scholar 

  8. Casa AM, Pressoir G, Brown PJ et al (2008) Community resources and strategies for association mapping in sorghum. Crop Sci 48:30–40

    Article  Google Scholar 

  9. Ehlert D, Horn H-J, Adamek R (2008) Measuring crop biomass density by laser triangulation. Comp Electron Agric 61(2):117–125. https://doi.org/10.1016/j.compag.2007.09.013

    Article  Google Scholar 

  10. Berni JAJ, Zarco-Tejada PJ, Suarez L et al (2009) Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans Geosci Remote Sens 47(3):722–738. https://doi.org/10.1109/TGRS.2008.2010457

    Article  Google Scholar 

  11. Thompson AL, Conrad A, Conley MM et al (2018) Professor: a motorized field-based phenotyping cart. HardwareX 4:e00025. https://doi.org/10.1016/j.ohx.2018.e00025

    Article  Google Scholar 

  12. Conley JWM (2013) A flexible, low-cost cart for proximal sensing. Crop Sci 53:1646–1649

    Article  Google Scholar 

  13. Andrade-Sanchez P, Gore MA, Heun JT et al (2014) Development and evaluation of a field-based high-throughput phenotyping platform. Funct Plant Biol 41:68–79

    Article  Google Scholar 

  14. Busemeyer L, Mentrup D, Möller K et al (2013) BreedVision - a multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors 13(3):2830

    Article  Google Scholar 

  15. Bao Y, Tang L, Breitzman MW et al (2019) Field-based robotic phenotyping of sorghum plant architecture using stereo vision. J Field Robot 36(2):397–415

    Article  Google Scholar 

  16. Young SN, Kayacan E, Peschel JM (2018) Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum. Precision Agric. https://doi.org/10.1007/s11119-018-9601-6

  17. Fan Z, Sun N, Qiu Q et al (2021) A high-throughput phenotyping robot for measuring stalk diameters of maize crops. 2021 IEEE 11th annual international conference on CYBER technology in automation, control, and intelligent systems (CYBER), 128–133

    Google Scholar 

  18. Atefi A, Ge Y, Pitla S, Schnable J (2021) Robotic technologies for high-throughput plant phenotyping: contemporary reviews and future perspectives. Front Plant Sci 12:611940

    Article  Google Scholar 

  19. Klose R, Penlington J, Ruckelshausen A (2009) Usability study of 3D time-of-flight cameras for automatic plant phenotyping. Bornimer Agrartechnische Berichte 69(12):93–105

    Google Scholar 

  20. Baharav T, Bariya M, Zakhor A (2017) Computing height and width of in situ sorghum plants using 2.5D infrared images. In: IS&T international symposium on electronic imaging computational imaging XV, Burlingame, CA, USA, pp 1–14

    Google Scholar 

  21. Pinto AM, Costa P, Moreira AP et al (2015) Evaluation of depth sensors for robotic applications. In: 2015 IEEE international conference on autonomous robot systems and competitions, 8-10 April 2015, pp 139–143. https://doi.org/10.1109/ICARSC.2015.24

    Chapter  Google Scholar 

  22. Day B, Bethel C, Murphy R et al (2008) A depth sensing display for bomb disposal robots. In: 2008 IEEE international workshop on safety, security and rescue robotics, Oct. 21-24 2008, pp 146–151. https://doi.org/10.1109/SSRR.2008.4745892

    Chapter  Google Scholar 

  23. Foix S, Alenya G, Torras C (2011) Lock-in time-of-flight (ToF) cameras: a survey. IEEE Sensors J 11(9):1917–1926. https://doi.org/10.1109/JSEN.2010.2101060

    Article  Google Scholar 

  24. Jiejie Z, Liang W, Ruigang Y et al (2008) Fusion of time-of-flight depth and stereo for high accuracy depth maps. In: 2008 IEEE conference on computer vision and pattern recognition, 23-28 June 2008, pp 1–8. https://doi.org/10.1109/CVPR.2008.4587761

    Chapter  Google Scholar 

  25. Li F, Chen H, Yeh C-K et al (2019) High spatial resolution time-of-flight imaging. In: Computational Imaging III, 2019. International Society for Optics and Photonics, p 1066908. https://doi.org/10.1117/12.2303794

    Chapter  Google Scholar 

  26. Chéné Y, Rousseau D, Lucidarme P et al (2012) On the use of depth camera for 3D phenotyping of entire plants. Comp Elect Agric 82:122–127

    Article  Google Scholar 

  27. Dhond UR, Aggarwal JK (1989) Structure from stereo-a review. IEEE Trans Syst Man Cybern 19(6):1489–1510

    Article  Google Scholar 

  28. Hartley R, Zisserman A (2003) Multiple view geometry in computer vision. Cambridge University Press, Cambridge, UK

    Google Scholar 

  29. Biskup B, Scharr H, Schurr U et al (2019) A stereo imaging system for measuring structural parameters of plant canopies - BISKUP - 2007. Plant Cell Environ 30(10):1299–1308. https://doi.org/10.1111/j.1365-3040.2007.01702.x

    Article  Google Scholar 

  30. Nguyen TT, Slaughter DC, Maloof JN et al (2016) Plant phenotyping using multi-view stereo vision with structured lights. In: SPIE commercial + scientific sensing and imaging, 17 may 2016 2016. Autonomous air and ground sensing systems for agricultural optimization and phenotyping. SPIE, p 9

    Google Scholar 

  31. Klodt M, Herzog K, Töpfer R et al (2015) Field phenotyping of grapevine growth using dense stereo reconstruction. BMC Bioinform 16(1):143. https://doi.org/10.1186/s12859-015-0560-x

    Article  Google Scholar 

  32. Song Y, Glasbey CA, van der Heijden GWAM et al (2011) Combining stereo and time-of-flight images with application to automatic plant phenotyping. In: Heyden A, Kahl F (eds) Image analysis. Springer, Berlin Heidelberg, pp 467–478

    Chapter  Google Scholar 

  33. Tuong Nguyen TC, Slaughter D, et al (2016) Comparison of structure-from-motion and stereo vision techniques for full in-field 3D reconstruction and phenotyping of plants: An investigation in sunflower. Paper presented at the 2016 ASABE Annual International Meeting, Orlando, Florida, Jul 17-20, 2016

    Google Scholar 

  34. Salas Fernandez MG, Bao Y et al (2017) A high-throughput, field-based phenotyping technology for tall biomass crops. Plant Physiol 174(4):2008–2022

    Article  Google Scholar 

  35. Li L, Zhang Q, Huang D (2014) A review of imaging techniques for plant phenotyping. Sensors 14(11):10.3390/s141120078

    Google Scholar 

  36. Jimenez-Berni JA, Deery DM, Rozas-Larraondo P et al (2018) High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR. Front Plant Sci 9(237). https://doi.org/10.3389/fpls.2018.00237

  37. Lin Y (2015) LiDAR: an important tool for next-generation phenotyping technology of high potential for plant phenomics? Comp Electron Agric 119:61–73

    Article  Google Scholar 

  38. Tilly N, Hoffmeister D, Cao Q et al (2014) Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice. Remote Sens 8(1):23. https://doi.org/10.1117/1.JRS.8.083671

    Article  Google Scholar 

  39. Saeys W, Lenaerts B, Craessaerts G et al (2009) Estimation of the crop density of small grains using LiDAR sensors. Biosyst Eng 102(1):22–30. https://doi.org/10.1016/j.biosystemseng.2008.10.003

    Article  Google Scholar 

  40. The Open Source Computer Vision (OpenCV) Library. https://opencv.org/

  41. Malik J, Belongie S, Leung T et al (2001) Contour and texture analysis for image segmentation. Internat J Comp Vision 43(1):7–27. https://doi.org/10.1023/A:1011174803800

    Article  Google Scholar 

  42. Li Z, Guo R, Li M et al (2020) A review of computer vision technologies for plant phenotyping. Comp Electron Agric 176:105672

    Article  Google Scholar 

  43. Gehan MA, Fahlgren N, Abbasi A et al (2017) PlantCV v2: image analysis software for high-throughput plant phenotyping. PeerJ 5. https://doi.org/10.7717/peerj.4088

  44. Jiang Y, Li C, Robertson JS et al (2018) GPhenoVision: a ground mobile system with multi-modal imaging for field-based high throughput phenotyping of cotton. Sci Rep 8(1):1213–1213. https://doi.org/10.1038/s41598-018-19142-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ćwiek H, Krajewski P, Klukas C et al (2015) Towards recommendations for metadata and data handling in plant phenotyping. J Exp Bot 66(18):5417–5427. https://doi.org/10.1093/jxb/erv271

    Article  CAS  PubMed  Google Scholar 

  46. Ćwiek-Kupczyńska H, Altmann T, Arend D et al (2016) Measures for interoperability of phenotypic data: minimum information requirements and formatting. Plant Methods 12(1):44. https://doi.org/10.1186/s13007-016-0144-4

    Article  PubMed  PubMed Central  Google Scholar 

  47. Garrido M, Paraforos SD, Reiser D et al (2015) 3D maize plant reconstruction based on georeferenced overlapping LiDAR point clouds. Remote Sens 7(12):10.3390/rs71215870

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC, USA

    Piyush Pandey

  2. Department of Civil and Environmental Engineering, Utah State University, Logan, UT, USA

    Sierra Young

Authors
  1. Piyush Pandey
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  2. Sierra Young
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Corresponding author

Correspondence to Sierra Young .

Editor information

Editors and Affiliations

  1. Arkansas Biosciences Institute, Arkansas State University, Jonesboro, AR, USA

    Argelia Lorence

  2. Arkansas Biosciences Institute, Arkansas State University, Jonesboro, AR, USA

    Karina Medina Jimenez

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© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Pandey, P., Young, S. (2022). Design Considerations for In-Field Measurement of Plant Architecture Traits Using Ground-Based Platforms. In: Lorence, A., Medina Jimenez, K. (eds) High-Throughput Plant Phenotyping. Methods in Molecular Biology, vol 2539. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2537-8_15

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  • DOI: https://doi.org/10.1007/978-1-0716-2537-8_15

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2536-1

  • Online ISBN: 978-1-0716-2537-8

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