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Leaf traits can provide important references for canopy light distribution, growth and development, and monitoring of external environment. Aiming at the problems of simplicity and abstraction in the process of processing and expressing leaf traits, a leaf traits fusion method based on morphological reconstruction was proposed. Taking the growth of cucumber leaves in greenhouse as an example, the effective accumulated temperature and growth rate were taken as characteristic parameters to establish the leaf morphogenesis model. The parametric spline curve was used to describe the geometric shape of leaf edge and vein. The dichotomy method was used to divide the leaf edge and vein curve recursively in order to realize the meshed subdivision of the blade surface. Combining with the leaf color texture information mapping model, a visual expression algorithm of leaf characteristics was introduced. The experimental verification results showed that the relative errors between the simulated and observed values of leaf traits obtained by this method were small, and the consistency was good, which demonstrated the method had certain feasibility and validity. Furthermore, in comparison with the typical statistical model and point cloud reconstruction model, the experimental results indicated that the square of correlation coefficients was above 0.95, and the root mean square deviation was no more than 0.236. Compared with traditional modeling methods, the proposed model had higher fitting degree and better reliability, by which it can effectively realize the dynamic simulation of cucumber leaf traits, which could provide a basis for realtime grasping and forecasting of plant growth and development. This method not only provided a reference for the dynamic tracking and management of greenhouse crop production, but also laid a theoretical foundation for the further study of the role of plants under various environmental factors.