Development of An Individual-Based Cotton Functional-Architectural Modeling System.

Cotton (Gossypium hirsutum L.) has an indeterminate growth pattern and exhibits marked plasticity in its growth, development, and architecture in response to different biotic and abiotic stress. It is one of the crop species that have been most extensively studied in terms of agronomy, physiology, crop modeling, and to a limited extent, architectural modeling. However, most of the modeling work has been based on an average plant or plant population, which limits the flexibility to incorporate traits that control the behavior of individual organs. The objective of this study was to develop an individual-based cotton plant modeling system that integrates a functional and an architectural component. The functional component of the system features an individual-based cotton model that incorporates major physiological processes and simulates the growth and development of individual organs (leaf blade, petiole, internode, branch, flower, fruit, and root). The architectural component of the system includes an architecture engine that constructs a 3D plant based on the physiological states of its organs. The architecture engine maps organ’s physiological states to architectural states, including topology (spatial arrangement) and geometry (shape, curvature, size), and creates a visual 3D image of the plant (i.e. visualization or rendering). Organ topology and geometry are based on plant architecture data collected from field studies. Rendering of the whole plant and the plant populations in 3D space is realized using Microsoft Visual Studio 2008 and Microsoft Windows Presentation Foundation. Organ microenvironment data (light capture and quality, temperature, etc.) can be estimated based on each organ’s 3D environment, and used as inputs to simulate organ processes in the physiological model. Currently, the system can only estimate the light captured by individual organs. Work is under way to extend the model to estimate other microenvironment data such as organ temperature and light quality.