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Research Article | Open Access

Bridging Time-series Image Phenotyping and Functional–Structural Plant Modeling to Predict Adventitious Root System Architecture

Sriram Parasurama1,3, Darshi Banan1, Kyungdahm Yun2, Sharon Doty1, Soo-Hyung Kim1( )
School of Environmental and Forest Sciences, University of Washington, Seattle, USA
Department of Smart Farm, Jeonbuk National University, Jeonju, Korea
School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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Abstract

Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional–structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection (P value = 0.0061, R2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.

References

1

Seethepalli A, Dhakal K, Griffiths M, Guo H, Freschet GT, York LM. RhizoVision explorer: Open-source software for root image analysis and measurement standardization. AoB PLANTS. 2021;13(6):plab056.
Crossref Google Scholar
2

Bauerle TL, Richards JH, Smart DR, Eissenstat DM. Importance of internal hydraulic redistribution for prolonging the lifespan of roots in dry soil. Plant Cell Environ. 2008;31(2):177–186.
Crossref Google Scholar
3

Freschet GT, Pagès L, Iversen CM, Comas LH, Rewald B, Roumet C, Klimešová J, Zadworny M, Poorter H, Postma JA, et al. A starting guide to root ecology: Strengthening ecological concepts and standardising root classification, sampling, processing and trait measurements. New Phytol. 2021;232(3):973–1122.
Crossref Google Scholar
4

Lynch JP. Harnessing root architecture to address global challenges. Plant J. 2022;109(2):415–431.
Crossref Google Scholar
5

Lynch JP. Root architecture and plant productivity. Plant Physiol. 1995;109(1):7–13.
Crossref Google Scholar
6

Bae H, Sicher RC, Kim MS, Kim S-H, Strem MD, Melnick RL, Bailey BA. The beneficial endophyte Trichoderma hamatum isolate DIS 219b promotes growth and delays the onset of the drought response in Theobroma cacao. J Exp Bot. 2009;60(11):3279–3295.
Crossref Google Scholar
7

Sultan S, Snider J, Conn A, Li M, Topp CN, Navlakha S. A statistical growth property of plant root architectures. Plant Phenomics. 2020;2020:2073723.
Crossref Google Scholar
8

Li A, Zhu L, Xu W, Liu L, Teng G. Recent advances in methods for in situ root phenotyping. PeerJ. 2022;10:Article e13638.
Crossref Google Scholar
9

Trachsel S, Kaeppler SM, Brown KM, Lynch JP. Shovelomics: High throughput phenotyping of maize (Zea mays L.) root architecture in the field. Plant Soil. 2011;341:75–87.
Crossref Google Scholar
10
Julkowska M. Plate assay for quantification of Root System Architecture of Arabidopsis seedlings. (2020).
11
Fridays Hands-On Workshop: Root scanning and image analysis using RhizoVision Explorer. (2020).
12

Schmidt JE, Lowry C, Gaudin ACM. An optimized Rhizobox protocol to visualize root growth and responsiveness to localized nutrients. J Vis Exp. 2018;(140):58674.
Crossref Google Scholar
13

Le Marié C, Kirchgessner N, Marschall D, Walter A, Hund A. Rhizoslides: Paper-based growth system for non-destructive, high throughput phenotyping of root development by means of image analysis. Plant Methods. 2014;10:13.
Crossref Google Scholar
14

Soualiou S, Wang Z, Sun W, de Reffye P, Collins B, Louarn G, Song Y. Functional–structural plant models Mission in advancing crop science: Opportunities and prospects. Front Plant Sci. 2021;12:747142.
Crossref Google Scholar
15

Vos J, Evers JB, Buck-Sorlin GH, Andrieu B, Chelle M, de Visser PHB. Functional–structural plant modelling: A new versatile tool in crop science. J Exp Bot. 2010;61(8):2101–2115.
Crossref Google Scholar
16

Schnepf A, Leitner D, Landl M, Lobet G, Mai TH, Morandage S, Sheng C, Zörner M, Vanderborght J, Vereecken H. CRootBox: A structural–functional modelling framework for root systems. Ann Bot. 2018;121(5):1033–1053.
Crossref Google Scholar
17

Yun K, Kim S-H. Cropbox: A declarative crop modelling framework. Silico Plants. 2023;5(1):diac021.
Crossref Google Scholar
18

Glass NT, Yun K, de Oliveira EAD, Zare A, Matamala R, Kim S-H, Gonzalez-Meler M. Perennial grass root system specializes for multiple resource acquisitions with differential elongation and branching patterns. Front Plant Sci. 2023;14:1146681.
Crossref Google Scholar
19

Postma JA, Kuppe C, Owen MR, Mellor N, Griffiths M, Bennett MJ, Lynch JP, Watt M. OpenSimRoot: Widening the scope and application of root architectural models. New Phytol. 2017;215(3):1274–1286.
Crossref Google Scholar
20

Lynch JP, Nielsen KL, Davis RD, Jablokow AG. SimRoot: Modelling and visualization of root systems. Plant Soil. 1997;188:139–151.
Crossref Google Scholar
21
Bleasdale, JKA. Vegetative propagation. In: Bleasdale JKA, editor. Plant physiology in relation to horticulture. London (UK): Macmillan Education UK; 1973. p. 89–104.
Crossref
22

Cronk QCB. Plant eco-devo: The potential of poplar as a model organism. New Phytol. 2005;166(1):39–48.
Crossref Google Scholar
23

Ewanick S, Bura R. A new approach to using dried hybrid poplar as a potential commodity feedstock for sugar production. ACS Sustain Chem Eng. 2016;4(8):4378–4384.
Crossref Google Scholar
24

Niemiec SS, Ahrens GR, Willits S, Hibbs DE. Hardwoods of the Pacific Northwest. Corvallis (OR): Oregon State University Press; 1995.
25

Bannoud F, Bellini C. Adventitious rooting in Populus species: Update and perspectives. Front Plant Sci. 2021;12:668837.
Crossref Google Scholar
26

Steffens B, Rasmussen A. The physiology of adventitious roots. Plant Physiol. 2016;170(2):603–617.
Crossref Google Scholar
27
Black Cottonwood (Populus trichocarpa) | Oregon Wood Innovation Center. https://owic.oregonstate.edu/black-cottonwood-populus-trichocarpa.
28
Petterson EB, Petterson NM, McLennan DS. Black cottonwood and balsam poplar Managers’ handbook for British Columbia. Victoria (Canada): Forestry Canada, Pacific Forestry Centre; 1996.
29

Albrecht U, Bordas M, Lamb B, Meyering B, Bowman KD. Influence of propagation method on root architecture and other traits of young citrus rootstock plants. HortScience. 2017;52(11):1569–1576.
Crossref Google Scholar
30

Dumroese RK, Luna T, Landis TD Nursery manual for native plants: A guide for tribal nurseries - volume 1: Nursery management. Washington (DC): USDA For Serv 1; 2009.
31

Radville L, McCormack ML, Post E, Eissenstat DM. Root phenology in a changing climate. J Exp Bot. 2016;67(12):3617–3628.
Crossref Google Scholar
32

Snedden J, Landhäusser SM, Lieffers VJ, Charleson LR. Propagating trembling aspen from root cuttings: Impact of storage length and phenological period of root donor plants. New For. 2010;39:169–182.
Crossref Google Scholar
33

McKown AD, Klápště J, Guy RD, El-Kassaby YA, Mansfield SD. Ecological genomics of variation in bud-break phenology and mechanisms of response to climate warming in Populus trichocarpa. New Phytol. 2018;220:300–316.
Crossref Google Scholar
34

Zalesny RS, Bauer EO, Riemenschneider DE. Use of belowground growing degree days to predict rooting of dormant hardwood cuttings of Populus. Silvae Genet. 2004;53(1–6):154–160.
Crossref Google Scholar
35
Riemenschneider DE, Bauer EO Quantitative genetic analysis of adventitious root forming ability in Populus trichocarpa. (Torr Et. Gray). In: Altman A, Waisel Y, editors. Biology of root formation and development. Boston (MA): Springer US; 1997. p. 193–201.
Crossref
36

Ritz C, Baty F, Streibig JC, Gerhard D. Dose-response analysis using R. PLOS ONE. 2015;10:Article e0146021.
Crossref Google Scholar
37
Home | AgWeatherNet at Washington State University. https://weather.wsu.edu/?
38
Hänninen H. Modelling bud dormancy release in trees from cool and temperate regions. ACTA Forestalia Fennica. 1990;(213):Article 7760.
Crossref
39

Harrington CA, Gould PJ, St.Clair JB. Modeling the effects of winter environment on dormancy release of Douglas-fir. For Ecol Manag. 2010;259(4):798–808.
Crossref Google Scholar
40
Climate Smart Farming CSF Growing Degree Day Calculator. http://climatesmartfarming.org/tools/csf-growing-degree-day-calculator/.
41
Germination Media - Seed Testing Paper - Anchor Paper - Minnesota. Anchor Paper https://www.anchorpaper.com/seed-solutions.
42

Epstein E, Bloom AJ. Mineral nutrition of plants: Principles and perspectives (Second Edition). Oxford (NY): Oxford University Press; 2004.
43
Hoagland DR, Arnon DI. The water-culture method for growing plants without soil. Berkeley (CA): University of California, College of Agriculture, Agricultural Experiment Station; 1938.
44

Abramoff MD, Magalhães PJ, Ram SJ. Image processing with Image. J Biophotonics Int. 2004;11(7):36–42.
Google Scholar
45

Seethepalli A, Guo H, Liu X, Griffiths M, Almtarfi H, Li Z, Liu S, Zare A, Fritschi FB, Blancaflor EB, et al. RhizoVision crown: An integrated hardware and software platform for root crown phenotyping. Plant Phenomics. 2020;2020:3074916.
Crossref Google Scholar
46

Lynch JP. Steep, cheap and deep: An ideotype to optimize water and N acquisition by maize root systems. Ann Bot. 2013;112(2):347–357.
Crossref Google Scholar
47

Schmidt JE, Gaudin ACM. Toward an integrated root Ideotype for irrigated systems. Trends Plant Sci. 2017;22:433–443.
Crossref Google Scholar
48
Mi G, Chen F, Yuan L, Zhang F. Chapter Two - Ideotype root system architecture for maize to achieve high yield and resource use efficiency in intensive cropping systems. In: Sparks DL, editor. Advances in agronomy. Beijing (China): Academic Press; 2016. vol. 139, p. 73–97.
Crossref
49
GeometryBasics.jl. (2023).
50

Danisch S, Krumbiegel J, Makie JL. Flexible high-performance data visualization for Julia. J Open Source Softw. 2021;6:3349.
Crossref Google Scholar
51
R: The R Project for Statistical Computing. https://www.r-project.org/.
52

Ruxton GD, Beauchamp G. Time for some a priori thinking about post hoc testing. Behav Ecol. 2008;19(3):690–693.
Crossref Google Scholar
53
Refaeilzadeh P, Tang L, Liu H. Cross-validation. In: Liu L., Özsu MT, editors. Boston (MA): Encyclopedia of database systems. Springer US; 2009. p. 532–538.
Crossref
54
Kassambara, A. rstatix: Pipe-Friendly Framework for Basic Statistical Tests (2023).
55
Haynes W. Bonferroni correction. In: Dubitzky W, Wolkenhauer O, Cho K-H, Yokota H, editors. Encyclopedia of systems biology. New York (NY): Springer; 2013. p. 154.
Crossref
56
Ting, K. M. Confusion matrix. In: Sammut C, Webb GI, editors. Encyclopedia of machine learning. Boston (MA): Springer US; 2010. p. 209.
Crossref
57
Adeleke E, Millas R, McNeal W, Faris J, Taheri A, Assessing Root System Architecture of Wheat Seedlings Using A High-Throughput Root Phenotyping System (2019).
Crossref
58

Maqbool S, Hassan MA, Xia X, York LM, Rasheed A, He Z. Root system architecture in cereals: Progress, challenges and perspective. Plant J. 2022;110(1):23–42.
Crossref Google Scholar
59

Strock CF, Burridge J, Massas ASF, Beaver J, Beebe S, Camilo SA, Fourie D, Jochua C, Miguel M, Miklas PN, et al. Seedling root architecture and its relationship with seed yield across diverse environments in Phaseolus vulgaris. Field Crops Res. 2019;237:53–64.
Crossref Google Scholar
60

Nagel KA, Putz A, Gilmer F, Heinz K, Fischbach A, Pfeifer J, Faget M, Blossfeld S, Ernst M, Dimaki C, et al. GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons. Funct Plant Biol. 2012;39(11):891–904.
Crossref Google Scholar
61

Arnaud M, Baird AJ, Morris PJ, Harris A, Huck JJ. EnRoot: A narrow-diameter, inexpensive and partially 3D-printable minirhizotron for imaging fine root production. Plant Methods. 2019;15:101.
Crossref Google Scholar
62

Rajurkar AB, McCoy SM, Ruhter J, Mulcrone J, Freyfogle L, Leakey ADB. Installation and imaging of thousands of minirhizotrons to phenotype root systems of field-grown plants. Plant Methods. 2022;18(1):39.
Crossref Google Scholar
63

Zhang S, Dai J, Ge Q. Responses of autumn phenology to climate change and the correlations of plant hormone regulation. Sci Rep. 2020;10:9039.
Crossref Google Scholar
64

Popko J, Hänsch R, Mendel R-R, Polle A, Teichmann T. The role of abscisic acid and auxin in the response of poplar to abiotic stress. Plant Biol. 2010;12(2):242–258.
Crossref Google Scholar
65

Edelmann HG. Plant root development: Is the classical theory for auxin-regulated root growth false? Protoplasma. 2022;259(3):823–832.
Crossref Google Scholar
66

Sun LR, Wang YB, He SB, Hao FS. Mechanisms for Abscisic acid inhibition of primary root growth. Plant Signal Behav. 2018;13:Article e1500069.
Crossref Google Scholar
67

Pilet P-E, Saugy M. Effect on root growth of endogenous and applied IAA and ABA. Plant Physiol. 1987;83(1):33–38.
Crossref Google Scholar
68

Crush JR, Care DA, Gourdin A, Woodfield DR. Root growth media effects on root morphology and architecture in white clover. N Z J Agric Res. 2005;48(2):255–263.
Crossref Google Scholar
69

Morandage S, Vanderborght J, Zörner M, Cai G, Leitner D, Vereecken H, Schnepf A. Root architecture development in stony soils. Vadose Zone J. 2021;20(4):Article e20133.
Crossref Google Scholar
70

Correa J, Postma JA, Watt M, Wojciechowski T. Soil compaction and the architectural plasticity of root systems. J Exp Bot. 2019;70(21):6019–6034.
Crossref Google Scholar
71

Li L, Deng X, Zhang T, Tian Y, Ma X, Wu P. Propagation methods decide root architecture of Chinese fir: Evidence from tissue culturing, rooted cutting and seed germination. Plants. 2022;11(19):2472.
Crossref Google Scholar
72

Iyer-Pascuzzi A, Symonova O, Mileyko Y, Hao Y, Belcher H, Harer J, Weitz JS, Benfey PN. Imaging and analysis platform for automatic phenotyping and trait ranking of plant root systems. Plant Physiol. 2010;152(3):1148–1157.
Crossref Google Scholar
73

Zhou X-R, Schnepf A, Vanderborght J, Leitner D, Lacointe A, Vereecken H, Lobet G. CPlantBox, a whole-plant modelling framework for the simulation of water- and carbon-related processes. Silico Plants. 2020;2(1):diaa001.
Crossref Google Scholar
74

Zhao J, Bodner G, Rewald B, Leitner D, Nagel KA, Nakhforoosh A. Root architecture simulation improves the inference from seedling root phenotyping towards mature root systems. J Exp Bot. 2017;68(5):965–982.
Crossref Google Scholar
Plant Phenomics
Volume 5,
2023
Article number: 0127
DOI: 10.34133/plantphenomics.0127
Cite this article:
Parasurama S, Banan D, Yun K, et al. Bridging Time-series Image Phenotyping and Functional–Structural Plant Modeling to Predict Adventitious Root System Architecture. Plant Phenomics, 2023, 5: 0127. https://doi.org/10.34133/plantphenomics.0127

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Received: 29 June 2023
Accepted: 21 November 2023
Published: 21 December 2023
© 2023 Sriram Parasurama et al. Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).
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