imaging; light gradient; controlled environment agriculture; red blue ratio; phenomics
Abstract :
[en] Background
The increasing demand for local food production is fueling high interest in the development of controlled environment agriculture. In particular, LED technology brings energy-saving advantages together with the possibility to manipulate plant phenotypes through light quality control. However, optimizing light quality is required for each cultivated plant and specific purpose.
Findings
In this paper, it is shown that the combination of LED gradient setups with imaging-based non-destructive plant phenotyping constitutes an interesting new screening tool with the potential to improve speed, logistics, and information output. To validate this concept, an experiment was performed to evaluate the effects of a complete range of Red:Blue ratios on seven plant species: Arabidopsis thaliana, Brachypodium distachyon, Euphorbia peplus, Ocimum basilicum, Oryza sativa, Solanum lycopersicum, and Setaria viridis. Plants were exposed during 30 days to the light gradient and showed significant, but species-dependent, responses in terms of dimension, shape, and color. A time series analysis of phenotypic descriptors highlighted growth changes but also transient responses of plant shapes to the Red:Blue ratio.
Conclusion
This approach, which generated a large reusable dataset, can be adapted for addressing specific needs in crop production or fundamental questions in photobiology.
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Bibliography
Gómez, C, Currey, CJ, Dickson, RW, et al. Controlled environment food production for urban agriculture. Hortscience 2019;54(9):1448-58.
Kozai, T. Plant factories with artificial lighting (PFALs): Benefits, problems, and challenges. In: T Kozai, ed. Smart Plant Factory. Singapore: Springer; 2018:doi:10.1007/978-981-13-1065-2-2.
SharathKumar, M, Heuvelink, E, Marcelis, LFM. Vertical farming: Moving from genetic to environmentalmodification. Trends Plant Sci 2020;25(8):724-7.
Cocetta, G, Casciani, D, Bulgari, R, et al. Light use efficiency for vegetables production in protected and indoor environments. Eur Phys J Plus 2017;132:43.
Mitchell, CA, Sheibani, F. Chapter 10 - LED advancements for plant-factory artificial lighting. In: T Kozai, G Niu, M Takagaki, eds. Plant Factory. Academic; 2020:167-84.
Bantis, F, Smirnakou, S, Ouzounis, T, et al. Current status and recent achievements in the field of horticulture with the use of light-emitting diodes (LEDs). Sci Hortic 2018;235: 437-51.
Chory, J. Light signal transduction: An infinite spectrum of possibilities. Plant J 2010;61(6):982-91.
Davis, PA, Burns, C. Photobiology in protected horticulture. Food Energy Secur 2016;5(4):223-38.
Paik, I, Huq, E. Plant photoreceptors: Multi-functional sensory proteins and their signaling networks. Semin Cell Dev Biol 2019;92:114-21.
Kusuma, P, Pattison, PM, Bugbee, B. From physics to fixtures to food: Current and potential LED efficacy. Hortic Res 2020;7: 56.
Folta, KM. Breeding new varieties for controlled environments. Plant Biol 2019;21:6-12.
Owen, WG, Lopez, RG. End-of-production supplemental lighting with red and blue light-emitting diodes (LEDs) influences red pigmentation of four lettuce varieties. Hortscience 2015;50(5):676-84.
Marondedze, C, Liu, X, Huang, S, et al. Towards a tailored indoor horticulture: A functional genomics guided phenotypic approach. Hortic Res 2018;5(1):doi:10.1038/s41438-018-0065-7.
Golzarian, MR, Frick, RA, Rajendran, K, et al. Accurate inference of shoot biomass fromhigh-throughput images of cereal plants. Plant Methods 2011;7(1):doi:10.1186/1746-4811-7-2.
Vasseur, F, Bresson, J, Wang, G, et al. Image-based methods for phenotyping growth dynamics and fitness components in Arabidopsis thaliana. Plant Methods 2018;14(1):doi:10.1186/s13007-018-0331-6.
Arend, D, Lange, M, Pape, J-M, et al. Quantitative monitoring of Arabidopsis thaliana growth and development using high-throughput plant phenotyping. Sci Data 2016;3(1):doi:10.1038/sdata.2016.55.
Laxman, RH, Hemamalini, P, Bhatt, RM, et al. Non-invasive quantification of tomato (Solanum lycopersicum L.) plant biomass through digital imaging using phenomics platform. Indian J Plant Physiol 2018;23(2):369-75.
Camargo, A, Papadopoulou, D, Spyropoulou, Z, et al. Objective definition of rosette shape variation using a combined computer vision and data mining approach. PLoS One 2014;9(5):e96889.
De Vylder, J, Vandenbussche, F, Hu, Y, et al. Rosette tracker: An open source image analysis tool for automatic quantification of genotype effects. Plant Physiol 2012;160(3):1149-59.
Vollmann, J, Walter, H, Sato, T, et al. Digital image analysis and chlorophyll metering for phenotyping the effects of nodulation in soybean. Comput Electron Agric 2011;75(1):190-5.
Hunt, ER, Doraiswamy, PC, McMurtrey, JE, et al. A visible band index for remote sensing leaf chlorophyll content at the canopy scale. Int J Appl Earth Obs Geoinf 2013;21:103-12.
Chen, D, Shi, R, Pape, J-M, et al. Predicting plant biomass accumulation from image-derived parameters. Gigascience 2018;7(2):doi:10.1093/gigascience/giy001.
Pieruschka, R, Schurr, U. Plant phenotyping: Past, present, and future. Plant Phenomics 2019;2019:doi:10.34133/2019/7507131.
Poorter, L. Growth responses of 15 rain-forest tree species to a light gradient: the relative importance of morphological and physiological traits. Funct Ecol 1999;13(3):396-410.
Kelly, J, Jose, S, Nichols, JD, et al. Growth and physiological response of six Australian rainforest tree species to a light gradient. Forest Ecol Manag 2009;257(1):287-93.
Cheng, X, Yu, M, Wang, G, et al. Morphology and biomass allocation in response to light gradient in five subtropical evergreen broadleaved tree seedlings. J Trop For Sci 2013;25(4):537-46.
Ouzounis, T, Rosenqvist, E, Ottosen, C-O. Spectral effects of artificial light on plant physiology and secondary metabolism: A review. Hortscience 2015;50(8):1128-35.
Jishi, T. LED lighting technique to control plant growth andmorphology. In: T Kozai, ed. Smart Plant Factory. Singapore: Springer; 2018:211-22.
Zhang, Y, Zhang, N. Imaging technologies for plant highthroughput phenotyping: A review. Front Agric Sci Eng 2018;5(4):doi:10.15302/J-FASE-2018242.
Tsaftaris, SA, Noutsos, C. Plant phenotyping with lowcost digital cameras and image analytics. In: IN Athanasiadis, AE Rizzoli, PA Mitkas, et al. Information Technologies in Environmental Engineering. Berlin, Heidelberg: Springer; 2009:doi:10.1007/978-3-540-88351-7-18.
Fahlgren, N, Gehan, MA, Baxter, I. Lights, camera, action: Highthroughput plant phenotyping is ready for a close-up. Curr Opin Plant Biol 2015;24:93-9.
Lien, MR, Barker, RJ, Ye, Z, et al. A low-cost and opensource platform for automated imaging. Plant Methods 2019;15(1):doi:10.1186/s13007-019-0392-1.
Sancho-Adamson, M, Trillas, M, Bort, J, et al. Use of RGB vegetation indexes in assessing early effects of verticillium wilt of olive in asymptomatic plants in high and low fertility scenarios. Remote Sens 2019;11(6):607.
Snowden, MC, Cope, KR, Bugbee, B. Sensitivity of seven diverse species to blue and green light: Interactions with photon flux. PLoS One 2016;11(10):e0163121.
Dieleman, JA, De Visser, PHB, Meinen, E, et al. Integrating morphological and physiological responses of tomato plants to light quality to the crop level by 3D modeling. Front Plant Sci 2019;10:doi:10.3389/fpls.2019.00839.
Fan, X, Zang, J, Xu, Z, et al. Effects of different light quality on growth, chlorophyll concentration and chlorophyll biosynthesis precursors of non-heading Chinese cabbage (Brassica campestris L.). Acta Physiol Plant 2013;35(9):2721-6.
Rabara, RC, Behrman, G, Timbol, T, et al. Effect of spectral quality of monochromatic LED lights on the growth of artichoke seedlings. Front Plant Sci 2017;8:doi:10.3389/fpls.2017.00190.
Hernández, R, Kubota, C. Physiological responses of cucumber seedlings under different blue and red photon flux ratios using LEDs. Environ Exp Bot 2016;121:66-74.
Dougher, TAO, Bugbee, B. Differences in the response of wheat, soybean and lettuce to reduced blue radiation. Photochem Photobiol 2007;73(2):199-207.
Piovene, C, Orsini, F, Bosi, S, et al. Optimal red:blue ratio in LED lighting for nutraceutical indoor horticulture. Sci Hortic 2015;193:202-8.
Naznin, MT, Lefsrud, M, Gravel, V, et al. Using different ratios of red and blue LEDs to improve the growth of strawberry plants. Acta Hortic 2016;1134:125-30.
Dou, H, Niu, G, Gu, M, et al. Morphological and physiological responses in basil and brassica species to different proportions of red, blue, and greenwavelengths in indoor vertical farming. J Am Soc Hortic Sci 2020;145(4):267-78.
Inoue, S, Kinoshita, T, Takemiya, A, et al. Leaf positioning of Arabidopsis in response to blue light. Mol Plant 2008;1(1):15-26.
Ouzounis, T, Heuvelink, E, Ji, Y, et al. Blue and red LED lighting effects on plant biomass, stomatal conductance, and metabolite content in nine tomato genotypes. Acta Hortic 2016;1134:251-8.
Hogewoning, SW, Trouwborst, G, Maljaars, H, et al. Blue light dose-responses of leaf photosynthesis, morphology, and chemical composition of Cucumis sativus grown under different combinations of red and blue light. J Exp Bot 2010;61(11):3107-17.
Barnes, C, Bugbee, B. Morphological responses of wheat to blue light. J Plant Physiol 1992;139(3):339-42.
Toyota, M, Tatewaki, N, Morokuma, M, et al. Tillering responses to high Red/Far-Red ratio of four Japanese wheat cultivars. Plant Prod Sci 2014;17(2):124-30.
Evers, JB, Vos, J, Andrieu, B, et al. Cessation of tillering in spring wheat in relation to light interception and red: Far-red ratio. Ann Bot 2006;97(4):649-58.
Mantilla-Perez, MB, Salas Fernandez, MG. Differential manipulation of leaf angle throughout the canopy: Current status and prospects. J Exp Bot 2017;68(21-22):5699-717.
Asahina, M, Tamaki, Y, Sakamoto, T, et al. Blue light-promoted rice leaf bending and unrolling are due to up-regulated brassinosteroid biosynthesis genes accompanied by accumulation of castasterone. Phytochemistry 2014;104:21-9.
Milivojevic, D, Eskins, K. Effect of light quality (blue, red) and fluence rate on the synthesis of pigments and pigment-proteins in maize and black pine mesophyll chloroplasts. Physiol Plant 1990;80(4):624-8.
Hunt, ER, Daughtry, CST, Eitel, JUH, et al. Remote sensing leaf chlorophyll content using a visible band index. Agron J 2011;103(4):1090-9.
Gracia-Romero, A, Kefauver, SC, Vergara-Díaz, O, et al. Comparative performance of ground vs. aerially assessed RGB and multispectral indices for early-growth evaluation of maize performance under phosphorus fertilization. Front Plant Sci 2017;8:doi:10.3389/fpls.2017.02004.
Vialet-Chabrand, S, Matthews, JSA, Simkin, AJ, et al. Importance of fluctuations in light on plant photosynthetic acclimation. Plant Physiol 2017;173(4):2163-79.
Morales, A, Kaiser, E. Photosynthetic acclimation to fluctuating irradiance in plants. Front Plant Sci 2020;11:doi:10.3389/fpls.2020.00268.
Wagner, R, Dietzel, L, Bräutigam, K, et al. The long-term response to fluctuating light quality is an important and distinct light acclimation mechanism that supports survival of Arabidopsis thaliana under low light conditions. Planta 2008;228(4):573-87.
Reuzeau, C, Frankard, V, Hatzfeld, Y, et al. Traitmill™: A functional genomics platform for the phenotypic analysis of cereals. Plant Genet Resour 2006;4(1):20-4.
Fiorani, F, Schurr, U. Future scenarios for plant phenotyping. Annu Rev Plant Biol 2013;64(1):267-91.
Li, Z, Guo, R, Li, M, et al. A review of computer vision technologies for plant phenotyping. Comput Electron Agric 2020;176:105672.
Paulus, S, Mahlein, A-K. Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale. Gigascience 2020;9(8):doi:10.1093/gigascience/giaa090.
Quantitative Plant. http://www.plant-image-analysis.org. Accessed 1 September 2020.
Lobet, G, Draye, X, Périlleux, C. An online database for plant image analysis software tools. Plant Methods 2013;9(1):38.
Opencv-python. https://pypi.org/project/opencv-python/. Accessed 1 November 2018.
Aravis. https://github.com/AravisProject/aravis. Accessed 1 November 2018.
Schneider, CA, Rasband, WS, Eliceiri, KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods 2012;9(7):671-5.
R for macOSX. https://cran.r-project.org/bin/macosx/. Accessed 1 September 2020.
Apogee, instruments. Owner's manual. Chlorophyll concentration meter. Model MC-100. 2019; https://www.apogeeinstruments.com/content/MC-100-manual.pdf. Accessed 1 February 2019.
Lejeune, P, Fratamico, A, Bouché, F, et al. Data and scripts used in the paper entitled "Led color gradient as a new screening tool for rapid phenotyping of plant responses to light quality" by Pierre LEJEUNE et al. Zenodo 2021:doi:10.5281/zenodo.4071810.
Lejeune, P, Fratamico, A. Data and R script used in the paper entitled "LED color gradient as a new screening tool for rapid phenotyping of plant responses to light quality" by Pierre LEJEUNE et al. Code Ocean 2021. https://doi.org/10.24433/CO.6400538.v2.
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