Abstract
The aim of this research is to integrate plant architectural modeling or “visualization modeling” and “mechanistic” or physiologically based modeling to describe how a real plant functions using a virtual crop. Virtual crops are life-like computer representations of crops based on individual plants and including the representation of the substrate on which the plants grow. The integration of a three-dimensional expression and the mechanistic model of plant development and growth requires the knowledge of the position of the organs along the different plant axes (the topology), their sizes, their forms, and their spatial orientation. The plant simulation model simulates the topology and organ weight or length. The superposition of spatial position and the topology produces the architecture of the plant. The association between sizes and organs creates what we refer to as the plant morphological model. Both components, the architectural model and the morphology model, are detailed in this paper. Once the integration is complete, the system produces a movie-like animation that shows the plant growing. The integrated model may simulate one or several plants growing simultaneously (in parallel). Visual capabilities make the proposed system very unique as it allows users to judge the results of the simulation the same way a farmer judges the situation of the crops in real life, by visually observing the field.
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References
Acock, B., Reddy, V. R., Hodges, H. F., Backer, D. N., & McKinion, J. M. (1985). Photosynthetic response of soybean canopies to full-season carbon dioxide enrichment. Agronomy Journal 77, 942–947.
Baker, D. N., Hesketh, J. D., et al. (1972). Simulation of growth and yield in cotton: I. Gross photosynthesis, respiration, and growth. Crop Science, 12, 431–435.
Baker, D. N., & Landivar, J. A. (1990). The simulation of plant development in GOSSYM. In T. Hodges (Ed.), Predicting crop phenology (pp. 153–170). Boca Raton, FL: CRC.
Baker, D., McKinion, J. M., et al. (1983). GOSSYM: A simulator of cotton crop growth and yield. South Carolina Agricultural Experiment Station Bulletin, 1089, 134.
Beer, F. P., & Johnson, E. R. J. (1981). Mechanics of materials. New York: McGraw-Hill.
Ben Porath, A. (1985). Effect of taproot restriction on growth and development of cotton grown under drip irrigation. Agronomy, Mississippi State, Mississippi State University, pp. 224.
Bouman, B. A. M., van Keulen, H., et al. (1996). The ‘School of de Wit’ crop growth simulation models: A pedigree and historical overview. Agricultural Systems, 52(2/3), 171–198. doi:10.1016/0308-521X(96)00011-X.
Bruce, R. R., & Römkens, M. J. M. (1965). Fruiting and growth characteristics of cotton in relation to soil moisture tension. Agronomy Journal, 57, 135–140.
Cox, P. G. (1996). Some issues in the design of agricultural decision support systems. Agricultural Systems, 52(2/3), 355–381. doi:10.1016/0308-521X(96)00063-7.
de Reffye, P. (1976). Modélisation et simulation de de la verse du caféier, à l'aide de la théorie de la résistance des matériaux. Cafe, Cacao, The, XX(4), 251–271.
de Reffye, P. (1979). Modélisation de l'architecture des arbres par des processus stochastistiques. Simulation spatiale des modèles tropicaux sous l'effet de la pesanteur. Application au coffea robusta. Orsay, France: Paris-Sud. p. 195.
de Reffye, P. (1983). Modèle mathématique aléatoire et simulation de la croissance et de l’architecture de caféier Robusta. 4eme partie. Programmation sur micro-ordinateur du tracé en trois dimensions de l’architecture d'un arbre. Application au caféier. Cafe, Cacao, The, XXVII(1), 3–20.
de Reffye, P., Blaise, F., et al. (1993). Modélisation et simulation de l’architecture et de la croissance des plantes. Revue. Palais de la Decouverte (Paris, France), 209, 23–48.
de Reffye, P., & Duceau, P.(1977). Amélioration de la résistance à la verse du caféier à l'aide de la théorie de la résistance des matériaux. In 8ème Colloque Scientifique International sur le Café, Abidjan, ASIC.
de Reffye, P., Edelin, C., et al. (1988). Plant models faithful to botanical structure and development. Computers & Graphics, 22(4), 151–158. doi:10.1145/378456.378505.
de Wit, C. T., & Brouwer, R.(1968). Über ein dynamisches Modell des vegetativen Wachstums von Pflanzenbeständen. Vereinigung für Angewandte Botanik (Personal document).
Duncan, W. G., Loomis, R. S., et al. (1967). A model for simulating photosynthesis in plant communities. Hilgardia. The Journal of Agricultural Science, 38, 181–205.
Egli, D. B. (1991). W.G. Duncan – father of crop models. Journal of Agronomic Education, 20(2), 167–167 Fall.
Fisher, J. B., & Honda, H. (1979). Branch geometry and effective leaf area: A study of terminalia branching pattern. I. Theoretical Trees. American Journal of Botany, 66, 633–644. doi:10.2307/2442408.
Fosner, R. (1997). OpenGL. Programming for Windows 95 and Windows NT. Redwood City, CA: Addison-Wesley.
Guinn, G. (1985). Fruiting of cotton. III. Nutritional stress and cutout. Crop Science, 25, 981–985.
Hesketh, J. D., Baker, D. N., et al. (1972). Simulation of growth and yield in cotton. II. Environmental control of morphogenesis. Crop Science, 12, 436–439.
Jallas, E. (1991). Modélisation de Développement et de la Croissance du Cotonnier. Mémoire de DEA en Agronomie. Paris: INA-PG, pp. 101.
Jallas, E. (1998). Improved model-based decision support by modeling cotton variability and using evolutionary algorithms. PhD dissertation in Biological Engineering, Mississippi State, Mississippi State University, pp. 239.
Kharche, S. D. (1984). Validation of GOSSYM: Effects of irrigation, leaf shape and plant population on canopy light interception, growth and yield of cotton. PhD dissertation in Agronomy, Mississippi State, Mississippi State University, pp. 173.
Lindenmayer, A. (1968). Mathematical models for cellular interaction in development, Parts I and II. Journal of Theoretical Biology, 18, 280–315. doi:10.1016/0022-5193(68)90079-9.
Marani, A., Baker, D. N., et al. (1985). Effect of water stress on canopy senescence and carbone exchange rates in cotton. Crop Science, 25, 798–802 September–October.
Papajorgji, P., & Pardalos, P. (2005). Software engineering techniques applied to agricultural systems an object-oriented and UML approach. New York: Springer.
Phene, C. J., Baker, D. N., et al. (1978). SPAR. A soil-plant–atmosphere-research system. Transactions of the ASAE, 21, 924–930.
Philipeau, G. (1995). Théorie des Plans d’Expérince. ITCF, Paris France.
Prusinkiewicz, P., & Hammel, M. (1994). Visual models of morphogenesis. http://www.cpsc.ucalgary.ca/projects/bmv/vmm/animations.html.
Reddy, K. R., Reddy, V. R., & Hodges, H. F. (1992). Temperature effects on early season cotton growth and development. Agronomy Journal, 84, 229–237.
Reddy, K. R., Hodges, H. F., & McKinion, J. M. (1993). Temperature effects on pima cotton leaf growth. Agronomy Journal, 85, 681–686.
Room, P. M., Hanan, J. S., et al. (1996). Virtual plants: New perspectives for ecologists, pathologists and agricultural scientists. Trends in Plant Science, 1(1), 33–38. doi:10.1016/S1360-1385(96)80021-5.
Room, P. M., Maillette, L., et al. (1994). Module and metamer dynamics and virtual plants. Advances in Ecological Research, 25, 105–157. doi:10.1016/S0065-2504(08)60214-7.
Saugier, B., & Garcia de Cortazar, V. (1987). Modélisation de la croissance d’une culture, relation potentielle avec la sélection. Le sélectionneur Français, 39, 7–18.
Sequeira, R. A., Olson, R. L., et al. (1996). An intelligent, interactive data input system for a cotton simulation system. AI Application, 10(1), 41–56.
Sinko, J. W., & Streifer, W. (1967). A new model for age-size structure of a population. Ecology, 48(6), 910–918. doi:10.2307/1934533.
Thanisawanyangkura, S., Sinoquet, H., et al. (1997). Leaf orientation and sunlit area distribution in cotton. Agricultural and Forest Meteorology, 86, 1–15.
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Appendix
Appendix
1.1 The Bending Model
1.1.1 Generality
The elasticity of a material depends on the applied forces, the section of the material, and an intrinsic characteristic named Young’s modulus [5]. For example, the elongation of a cylindrical bar can be expressed as:
where: h − h 0 is the elongation, S the section, F the traction force applied at the end of the bar, and E the Young’s modulus.
A fruiting branch can be assimilated to an embedded bar in the mainstem and submitted to a vertical force F (see below Fig. 15).
At the point of coordinates (x,y) by definition, the flexing moment M is:
where: E is the Young module, I is the inertia (\({\text{{\rm I}}} = \frac{{\pi {\text{R}}^4 }}{4}\) in the case of a bar with circular section, where R is the radius of the section), dS a small length of the bar, dθ the angle between the tangent of two successive length portions of the bar, and \(\frac{{d\theta }}{{d{\text{S}}}}\) the bending of the bar.
At the end of the bar where bending force is applied, the flexing moment is nil. At the point of (x, y) coordinates, the flexing moment M and the bending force equilibrate (see Fig. 15). This can be expressed as:
where: F cosθ 0 is the “compression” component and F sinθ 0 is the “flexion” component.
By differentiation of Eq. 19 with respect to S, we obtain:
where: \(\frac{{d{\text{y}}}}{{d{\text{S}}}} = \sin \theta \) and \(\frac{{d{\text{x}}}}{{d{\text{S}}}} = \cos \theta \) (see Fig. 15) so Eq. 20 can be expressed as:
By multiplying Eq. 21 by \(\frac{{d\theta }}{{d{\text{S}}}}\), we obtain:
Then, by integration between w, the angle at the end of the bar, to θ with respect to S, because the flexion moment at the end of the bar is nil, we obtain:
Then:
where: \({\text{K = }}\sqrt {\frac{{\text{F}}}{{{\text{E}} \times {\text{{\rm I}}}}}} \)
1.1.2 Case of small flexion
In the case of small flexion, w is small, θ is small too, and θ 0 varies from 0 to π. Equation 24 can be expressed as:
where: \({\text{A}} = \cos \left( {\theta _0 + \theta } \right) - \cos \left( {\theta _0 + {\text{w}}} \right),\)
Because θ is small, we can approximate cosθ by \(\left( {1 - \frac{{\theta ^2 }}{2}} \right)\) (from Maclaurin’s formula, n = 2), cosw by \(\left( {1 - \frac{{{\text{w}}^2 }}{2}} \right)\), sinθ by θ (from Maclaurin’s formula, n = 2), and sin w by w. Then, A can be expressed as:
if: \({\text{a}} = {\text{w}} \times \left( {\sin \theta _0 + {\text{w}}\frac{{\cos \theta _0 }}{2}} \right)\), \({\text{b}} = - \left( {\sin \theta _0 } \right)\) and \({\text{c}} = - \left( {\frac{{\cos \theta _0 }}{2}} \right)\),
then: A = a + b × θ + c × θ 2. Equation 25 can be expressed as:
We can express:
with:
Now, we have:
Replacing in Eq. 26, one obtains:
In the last expression, we can recognize that A is expressed in function of a constant a and a variable x. Then A is in the form of: A = a 2 − x 2, so Eq. 27 can be expressed as:
where:
In Eq. (31), x varies from \(\frac{{\sin \theta _0 }}{{\sqrt {2\cos \theta _0 } }}\) to \({\text{w}} \times \sqrt {\frac{{\sin \theta _0 }}{2}} + \frac{{\sin \theta _0 }}{{\sqrt {2\cos \theta _0 } }}\), because θ varies from 0 to w. By differentiation with respect to θ, Eq. 31 gives:
When replacing dθ by dx in Eq. 27, we obtain:
The length of the branch is h (see Fig. 15) so the integration with respect to S of Eq. 32 gives:
By definition:
then, Eq. 33 can be expressed as:
Solving for w gives:
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Jallas, E., Sequeira, R., Martin, P. et al. Mechanistic Virtual Modeling: Coupling a Plant Simulation Model with a Three-dimensional Plant Architecture Component. Environ Model Assess 14, 29–45 (2009). https://doi.org/10.1007/s10666-008-9164-4
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DOI: https://doi.org/10.1007/s10666-008-9164-4