Abstract: Abstract: Virtual rice technology has played an important role in modern agricultural production decision-making, yield prediction, crop breeding and growth conditions optimization. In the visualization of virtual rice, the phenomenon of interpenetration between organs is often found. There is need to use collision detection technology to avoid this phenomenon. However, when the number and size of the population increase, there will be a lower collision detection efficiency. To improve the efficiency of leaf collision detection during the dynamic growth simulation of rice population, methods for rapid construction of mixed leaf tree (MLT) and fast detection of CPU/GPU are proposed in this paper. The main ideas of these methods are based on the morphological structure characteristics of leaf parabola and the accelerating characteristics of CPU/GPU. On the single leaf scale, a new calculation of OBB bounding box direction axis method is proposed to reduce the construction complexity of OBB bounding box, and the MLT is constructed based on the upper axis aligned bounding box (AABB) and the lower oriented bounding box (OBB). The AABB bounding box is built to quickly exclude the disjointed leaf pairs and the OBB bounding box is built to ensure the accuracy of the collision detection. According to the morphological characteristics of rice leaves, the calculation method of the new OBB bounding box direction axis is proposed to replace the traditional calculation method based on covariance matrix and mean value, which reduces the complexity of OBB bounding box construction. The new calculation method is applicable to the cases in which the leaves of rice are not twisted, curled, broken, and so on. In these cases, the mid-vein curve of the rice leaves can be considered as the first order derivable and the second order continuous. The connection line between the starting point and the end point in the mid-vein curve is a direction axis of the OBB bounding box, in the initial structure of rice leaves, the z-axis is the other axis of the leaf OBB bounding box, and the third direction axis can be determined by the 2 determined direction axes. On the group scale, firstly, according to the regularity of rice population cultivation and the law of rice growth, the rule of collision detection between rice is proposed: No collision detection is calculated between rows or columns of non-adjacent plants, and if row or column distance is greater than the sum of the length of the 2 longest leaves of rice, there is no collision detection between them. Use these rules to reduce the number of rice leaves for collision detection. Then the CPU/GPU acceleration scheme was designed by using the dependence of the collision detection between the leaves of the individual plants and rice population: The construction of the rice population MLT on the CPU side is carried out, and the intersection of the calculation on the GPU side is calculated. Each thread block represents the result of the intersection detection of a leaf, and for each thread in the thread block a crossing detection is calculated between a pair of leaves; when the thread block in the calculation of all the results has no collision, it is determined that thread block represents no collision, otherwise it is determined to have the collision. After all the calculation is completed, the intersection test result is returned to the CPU side for processing. In order to verify the effectiveness of this study, the experiment of collision detection efficiency was carried out with the leaves of large-scale rice population at tillering stage. The results show that the time consumed by collision detection method proposed in this paper is 50% less than the traditional AABB and OBB method, which effectively improves the collision detection speed between leaves. When the rice population is large, the running time of CPU/GPU parallel acceleration is 98% less than that of the CPU, and the collision detection efficiency is greatly improved.