Abstract
Detecting and resolving the collision of organs between different plants or the collision of different organs of a single plant are key issues in the realistic construction of a virtual plant population. A suitable collision detection scheme is necessary to prevent a reduction in realism caused by organ penetration. A mixed bounding volume tree construction scheme based on the growth characteristics of tomato plants is proposed in this paper, and the construction mode of the bounding at all levels is simplified by using a digital tomato model. Using a parallel GPU approach, we designed a tomato plant population collision detection program with CUDA acceleration. The proposed method reduces the total collision detection time by 92%-96%. Keywords: plant simulation, collision detection, bounding volume, GPU processing DOI: 10.25165/j.ijabe.20191206.4888 Citation: Ding W L, Wan Z X, Xu Y, Max N. New collision detection method for simulating virtual plant populations. Int J Agric & Biol Eng, 2019; 12(6): 156–151.
Highlights
Plant populations are common in nature and one of the basic elements of virtual scenes
An accurate virtual plant population model can provide a platform for research on many agricultural science problems[1], such as optimization of crop spacing and density and cultivation of an ideal plant type[2]
Typical organs considered in 3D models are stems, leaves, flowers, and fruits
Summary
Plant populations are common in nature and one of the basic elements of virtual scenes. Establishing a realistic 3D model for this type of scene is difficult because of the complex morphological structure of plant populations. The efficiency of a collision detection algorithm is a serious issue due to a large number of organs in a plant population. Many collision detection algorithms for large-scale scenes focused on the collisions among different plants, but few related with the internal organs in a plant. The contribution of this article is an optimization algorithm for the bounding volume tree of tomato plants, based on the morphological characteristics of tomato plants and using parallel computing technology. An optimized collision detection process was designed according to the morphological characteristics of tomato for different hierarchical bounding volumes. A plant structure tree was built and applied to internal organ collision culling of tomato by using the structure of tomato plant models. The efficiency of the algorithm was improved via CUDA parallel acceleration
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