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Time Change in Spatial Distributions of Light Interception and Photosynthetic Rate of Paprika Estimated by Ray-tracing Simulation

광 추적 시뮬레이션에 의한 시간 별 파프리카의 수광 및 광합성 속도 분포 예측

  • Kang, Woo Hyun (Department of Plant Science and Research Inst. of Agricultural and Life Sci., Seoul National University) ;
  • Hwang, Inha (Department of Plant Science and Research Inst. of Agricultural and Life Sci., Seoul National University) ;
  • Jung, Dae Ho (Department of Plant Science and Research Inst. of Agricultural and Life Sci., Seoul National University) ;
  • Kim, Dongpil (Department of Plant Science and Research Inst. of Agricultural and Life Sci., Seoul National University) ;
  • Kim, Jaewoo (Department of Plant Science and Research Inst. of Agricultural and Life Sci., Seoul National University) ;
  • Kim, Jin Hyun (Protected Horticulture Research Institute, National Institute of Horticultural and Herbal Science) ;
  • Park, Kyoung Sub (Department of Horticultural Science, Mokpo National University) ;
  • Son, Jung Eek (Department of Plant Science and Research Inst. of Agricultural and Life Sci., Seoul National University)
  • 강우현 (서울대학교 식물생산과학부) ;
  • 황인하 (서울대학교 식물생산과학부) ;
  • 정대호 (서울대학교 식물생산과학부) ;
  • 김동필 (서울대학교 식물생산과학부) ;
  • 김재우 (서울대학교 식물생산과학부) ;
  • 김진현 (국립원예특작과학원 시설원예연구소) ;
  • 박경섭 (목포대학교 원예과학과) ;
  • 손정익 (서울대학교 식물생산과학부)
  • Received : 2019.04.20
  • Accepted : 2019.09.09
  • Published : 2019.10.30

Abstract

To estimate daily canopy photosynthesis, accurate estimation of canopy light interception according to a daily solar position is needed. However, this process needs a lot of cost, time, manpower, and difficulty when measuring manually. Various modeling approaches have been applied so far, but it was difficult to accurately estimate light interception by conventional methods. The objective of this study is to estimate the spatial distributions of light interception and photosynthetic rate of paprika with time by using 3D-scanned plant models and optical simulation. Structural models of greenhouse paprika were constructed with a portable 3D scanner. To investigate the change in canopy light interception by surrounding plants, the 3D paprika models were arranged at $1{\times}1$ and $9{\times}9$ isotropic forms with a distance of 60 cm between plants. The light interception was obtained by optical simulation, and the photosynthetic rate was calculated by a rectangular hyperbola model. The spatial distributions of canopy light interception of the 3D paprika model showed different patterns with solar altitude at 9:00, 12:00, and 15:00. The total canopy light interception decreased with an increase of surrounding plants like an arrangement of $9{\times}9$, and the decreasing rate was lowest at 12:00. The canopy photosynthetic rate showed a similar tendency with the canopy light interception, but its decreasing rate was lower than that of the light interception due to the saturation of photosynthetic rate of upper leaves of the plants. In this study, by using the 3D-scanned plant model and optical simulation, it was possible to analyze the light interception and photosynthesis of plant canopy under various conditions, and it can be an effective way to estimate accurate light interception and photosynthesis of plants.

작물의 일중 광합성량을 정확하게 추정하기 위해서는 일중 태양의 위치 변화에 따른 작물의 정확한 수광량 변화를 정확하게 예측해야 한다. 그러나, 이는 많은 시간, 비용, 노력이 소요되며, 측정의 어려움이 수반된다. 현재까지 다양한 모델링 기법이 적용되었으나 기존 방식으로는 정확한 수광 예측이 어려웠다. 본 연구의 목적은 파프리카의 3차원 스캔 모델과 광학 시뮬레이션을 이용하여 일중 시간 별 캐노피 수광 분포와 광합성 속도의 변화를 예측하는 것이다. 휴대용 3차원 스캐너를 이용하여 온실에서 재배되는 파프리카의 구조 모델을 구축하였다. 주변 개체의 유무에 따른 캐노피 수광 분포의 변화를 보기 위하여 작물 모델 별 간격을 60cm로 $1{\times}1$, $9{\times}9$ 정방형 배치하여 광학 시뮬레이션을 수행하였다. 광합성 속도는 직각쌍곡선 모델을 이용하여 계산하였다. 3차원 파프리카 모델 표면의 수광 분포는 오전 9시, 정오, 오후 3시의 태양 각도에 따라 서로 다른 양상을 보였다. 캐노피 총 수광량은 $9{\times}9$ 배치로 주변 개체 수가 늘어남에 따라 감소하였고, 태양 고도가 가장 높은 정오에서의 감소율이 가장 적었다. 캐노피 광합성 속도와 $CO_2$ 소모량 역시 수광량과 비슷한 양상을 보였으나 작물 상단부 엽의 광합성 속도 포화로 인해 수광량 변화에 비해 적은 감소율을 보였다. 본 연구에서는 파프리카의 3차원 스캔 모델과 광학 시뮬레이션을 이용하여 가상 환경 조건에서의 캐노피 수광과 광합성 분포를 분석할 수 있었으며, 이는 추후 다양한 재배 조건에서 작물 수광량과 광합성 속도를 예측하는 데에 효과적으로 활용될 수 있을 것으로 사료된다.

Keywords

References

  1. Acock, B., J.H.M. Thornley, J.W. Wilson. 1971. Photosynthesis and energy conversion. In: Wareing PF, Cooper JP (Eds.), Potential crop production. Heinemmann Educational Publishers, London, U.K., pp 43-75.
  2. Behmann, J., A.K. Mahlein, S. Paulus, J. Dupuis, H. Kuhlmann, E.C. Oerke, L. Plumer. 2016. Generation and application of hyperspectral 3D plant models: Methods and challenges. Mach. Vision Appl. 27:611-624. https://doi.org/10.1007/s00138-015-0716-8
  3. Buck-Sorlin, G.H., P.H.B. De Visser, M. Henke, V. Sarlikioti, G.W.A.M. van der Heijden, L.F.M. Marcelis, J. Vos. 2011. Towards a functional-structural plant model of cut-rose: Simulation of light environment, light absorption, photosynthesis, and interference with the plant structure. Ann. Bot. 108:1121-1134. https://doi.org/10.1093/aob/mcr190
  4. Caldwell, M.M., H.P. Meister, J.D. Tenhunen, O.L. Lange. 1986. Canopy structure, light microclimate, and leaf gas exchange of Quercus coccifera L. in a Portuguese macchia: Measurements in different canopy layers and simulations with a canopy model. Trees 1:25-41. https://doi.org/10.1007/BF00197022
  5. Chen, J.M., J. Liu, J. Cihlar, M.L. Goulden. 1999. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol. Model. 124:99-119. https://doi.org/10.1016/S0304-3800(99)00156-8
  6. De Visser, P.H.B., G.H. Buck-Sorlin, G.W.A.M. van der Heijden. 2014. Optimizing illumination in the greenhouse using a 3D model of tomato and a ray tracer. Front. Plant Sci. 5:48. https://doi.org/10.3389/fpls.2014.00048
  7. Goudriaan, J. 1995. Optimization of nitrogen distribution and leaf area index for maximum canopy assimilation rate. Nitrogen Management Studies in Irrigated Rice, pp 85-97.
  8. Hilker, T., N.C. Coops, C.R. Schwalm, R.P.S. Jassal, T.A. Black, P. Krishnan. 2008. Effects of mutual shading of tree crowns on prediction of photosynthetic light-use efficiency in a coastal Douglas-fir forest. Tree Physiol. 28:825-834. https://doi.org/10.1093/treephys/28.6.825
  9. Hu, E., L. Tong, D. Hu, H. Liu. 2011. Mixed effects of $CO_2$ concentration on photosynthesis of lettuce in a closed artificial ecosystem. Ecol. Eng. 37:2082-2086. https://doi.org/10.1016/j.ecoleng.2011.08.012
  10. Jung, D.H., D. Kim, H.I. Yoon, T.W. Moon, K.S. Park, J.E. Son. 2016. Modelling the canopy photosynthetic rate of romaine lettuce (Lactuca sativa L.) grown in a plant factory at varying $CO_2$ concentrations and growth stages. Hortic. Environ. Biotechnol. 57:487-492. https://doi.org/10.1007/s13580-016-0103-z
  11. Jung, D.H., J.W. Lee, W.H. Kang, I. Hwang, J.E. Son. 2018. Estimation of whole plant photosynthetic rate of Irwin mango under artificial and natural lights using a threedimensional plant model and ray-tracing. Intl. J. Mol. Sci. 19:152. https://doi.org/10.3390/ijms19010152
  12. Kim, J.H., J.W. Lee, T.I. Ahn, J.H. Shin, K.S. Park, J.E. Son. 2016. Sweet pepper (Capsicum annuum L.) canopy photosynthesis modelling using 3D plant architecture and light ray-tracing. Front. Plant Sci. 7:1321.
  13. Li T., E. Heuvelink, T.A. Dueck, J. Janse, G. Gort, L.F.M. Marcelis. 2014. Enhancement of crop photosynthesis by diffuse light: Quantifying the contributing factors. Ann. Bot. 114:145-156. https://doi.org/10.1093/aob/mcu071
  14. Marcelis, L.F.M., E. Heuvelink, J. Goudriaan. 1998. Modelling biomass production and yield of horticultural crops: A review. Sci. Hortic. 74:83-111. https://doi.org/10.1016/S0304-4238(98)00083-1
  15. Monsi, M. and T. Saeki. 1953. The light factor in plant communities and its significance for dry matter production. Jpn. J. Bot. 14:22-52.
  16. Monteith, J.L. 1965. Light distribution and photosynthesis in field crops. Ann. Bot. 29:17-37. https://doi.org/10.1093/oxfordjournals.aob.a083934
  17. Moriondo, M., L. Leolini, N. Stagliano, G. Argenti, G. Trombi, L. Brilli, M. Bindi. 2016. Use of digital images to disclose canopy architecture in olive tree. Sci. Hortic. 209:1-13. https://doi.org/10.1016/j.scienta.2016.05.021
  18. Paulus, S., H. SchumaJnn, H. Kuhlmann, J. Leon. 2014. Highprecision laser scanning system for capturing 3D plant architecture and analyzing growth of cereal plants. Biosyst. Eng. 121:1-11. https://doi.org/10.1016/j.biosystemseng.2014.01.010
  19. Prusinkiewicz, P. 1986. Graphical applications of L-systems. Proc. Graphics Interface 86:247-253.
  20. Retkute R., A.J. Townsend, E.H. Murchie, O.E. Jensen, S.P. Preston. 2018. Three-dimensional plant architecture and sunlit-shaded patterns: A stochastic model of light dynamics in canopies. Ann. Bot. 122:291-302. https://doi.org/10.1093/aob/mcy067
  21. Sarlikioti, V., P.H.B. De Visser, G.H. Buck-Sorlin, L.F.M. Marcelis. 2011. How plant architecture affects light absorption and photosynthesis in tomato: Towards an ideotype for plant architecture using a functional-structural plant model. Ann. Bot. 108:1065-1073. https://doi.org/10.1093/aob/mcr221
  22. Smith, A.R. 1984. Plants, fractals, and formal languages. ACM SIGGRAPH Computer Graphics 18:1-10. https://doi.org/10.1145/964965.808571
  23. Son, J.E., J.S. Park, H.Y. Park. 1999. Analysis of carbon dioxide changes in urban-type plant factory system. Hortic. Environ. Biotechnol. 40:205-208.
  24. Tanaka, A. and K. Kawano. 1966. Effect of mutual shading on dry-matter production in the tropical rice plant. Plant Soil 24:128-144. https://doi.org/10.1007/BF01373079
  25. Thornley, J.H.M. 1974. Light fluctuations and photosynthesis. Ann. Bot. 38:363-373. https://doi.org/10.1093/oxfordjournals.aob.a084820
  26. Thornley, J.H.M. 1976. Mathematical models in plant physiology. Academic Press Inc. Ltd. London, U.K., p 318.
  27. Vos, J., L.F.M. Marcelis, J.B. Evers. 2007. Functional-structural plant modelling in crop production: adding a dimension. Frontis 22:1-129.
  28. Wahabzada, M., S. Paulus, K. Kersting, A.K. Mahlein. 2015. Automated interpretation of 3D laser scanned point clouds for plant organ segmentation. BMC Bioinformatics 16:248. https://doi.org/10.1186/s12859-015-0665-2
  29. Zhang, Y., P. Teng, Y. Shimizu, F. Hosoi, K. Omasa. 2016. Estimating 3D leaf and stem shape of nursery paprika plants by a novel multi-camera photography system. Sensors 16:874. https://doi.org/10.3390/s16060874