  Landscape renderings are realistic images of landscape sites, allowing
stakeholders to perceive better and evaluate design ideas. While recent
advances in Generative Artificial Intelligence (GAI) enable automated
generation of landscape renderings, the end-to-end methods are not compatible
with common design processes, leading to insufficient alignment with design
idealizations and limited cohesion of iterative landscape design. Informed by a
formative study for comprehending design requirements, we present PlantoGraphy,
an iterative design system that allows for interactive configuration of GAI
models to accommodate human-centered design practice. A two-stage pipeline is
incorporated: first, concretization module transforms conceptual ideas into
concrete scene layouts with a domain-oriented large language model; and second,
illustration module converts scene layouts into realistic landscape renderings
using a fine-tuned low-rank adaptation diffusion model. PlantoGraphy has
undergone a series of performance evaluations and user studies, demonstrating
its effectiveness in landscape rendering generation and the high recognition of
its interactive functionality.
