Plants in a digital medium are commonly represented through various artistic expressions such as video game landscapes and simulated research environments. Plant growth modeling, in the visual sense, is a complex process that combines artistic skills with procedurally generated rendering algorithms to achieve perceptually "realistic" outcomes. While this may be enough for applications including seemingly realistic and visually aesthetic plant growth, it does not capture the true plant's trajectory. Plant systems involve dynamic interactions with environmental conditions and other plants, making them gray or black box systems that can be challenging to capture via a simple linear or polynomial model. This leads us to use rudimentary methods such as the prominent Lindenmayer Systems which still requires pre-rendered art to visually represent the plant. I am proposing a research project as part of the B21 BLUE fellowship which explores an encoded representation akin to Neural Radiance Fields for digital plant representation, and a generative model approach for sampling the encoded latent space to construct realistic rendering of a plant's growth cycle.