Abstract: Abstract: Crop colony is the organization system which performs photosynthesis and dry matter production function. Its morphological structure has important influence on light interception ability, canopy photosynthetic efficiency and crop yield. The morphological characteristics of crop colony have always been the most basic way for people to recognize, analyze and evaluate crops. Therefore, it is of great practical significance to rapidly and accurately model and analyze the morphology of crop colony in a digital and visual way. Morphological data acquisition of maize colony is labor-intensive and time-consuming, and thus a t-distribution based three-dimensional (3D) maize colony modeling method was proposed using a few measured data. The method constructs t-distribution function of primary plant morphological parameters using measured data and generates random plant morphological parameters under the constraint. The main plant morphological parameters include plant and phytomer scale. Here plant scale parameters include plant height, total leaf number, and first leaf index, and phytomer scale parameters include leaf growth height, leaf insertion angle, leaf length, leaf width, and leaf azimuthal angle. Particularly, leaf azimuthal angles are generated using the deviations between the plant azimuthal plane and leaf azimuths. High quality geometric models in 3D template resource database of maize organs are selected by constructing a similarity assess function of plant morphological parameters. Leaf length, leaf insertion angle, leaf index, and plant cultivar are the control parameters in the function. Then geometric models of individual plants in target colony are generated. Interactive design or field image extraction method is used to allocate the growth positions and plant azimuthal planes of each plant in the colony. Maize colony is generated by moving and rotating operations of each plant according to the designed or extracted growth positions and plant azimuthal planes. Leaf area index (LAI) is used to validate the generated maize colony model. Three in-situ field measurement experiments in Qitai County of Xinjiang using 3D digitizer were carried out to reconstruct geometric models of maize colony, and the cultivar was Xianyu 335 and the planting densities were 105, 135, and 165 thousand plants/hm2, as true values for LAI calculating. Corresponding plant morphological parameters of the corresponding colonies were measured. The maize colony modeling method based on t-distribution function was used to construct 3D models and LAI was also calculated for the colonies. Results show that the LAI errors are less than ±2%. In addition, generalized LAI of different heights of plant colony is proposed to provide more detailed verification in different height levels. The averaged RMSE (root mean square error) of Xianyu 335 with the density of 135 thousand plants/hm2 is 0.023, and the averaged NRMSE (normalized root mean square error) is 0.425, which demonstrate that it has a good consistency of spatial leaf distribution between the in-situ measured field colony and reconstructed colony using t-distribution. These results show that the proposed maize colony modeling method could meet the needs of plant functional-structural analysis. Compared with the existing methods, the proposed method is more effective and highly realistic, and the constructed maize colonies are capable of reflecting the agronomic characteristics of the target colony, such as the differences caused by intrinsic cultivar, environment, planting, or management factors. Maize colony model could be rapidly generated by simple modification of morphological input parameters. Combined with the light distribution simulating algorithm, a large number of maize colony models will be designed for virtual experiments. It has great importance for the research and application of maize plant morphology optimization, estimation of planting density, adaptability evaluation of different cultivars, and cultivation strategy decision. Due to the complexity of maize colony structure morphology, there are still many subsequent colony modeling issues that will be addressed in future research, such as adjacent phytomer parameters constraint model construction, plant collision detection and collision response, and colony mesh simplification and optimization for visual computing.