Generative Plant Growth Simulation from Sequence-Informed Environmental Conditions

TL;DR

This paper presents SI-PGS, a probabilistic generative framework that models plant growth over time by integrating environmental data, enabling realistic and coherent simulation of plant development.

Contribution

The paper introduces a novel sequence-informed generative model for plant growth simulation that captures temporal dependencies using a fusion of sensor and contextual data.

Findings

Successfully captures temporal dependencies in plant growth

Generates realistic and coherent plant development sequences

Outperforms baseline models in simulation quality

Abstract

A plant growth simulation can be characterized as a reconstructed visual representation of a plant or plant system. The phenotypic characteristics and plant structures are controlled by the scene environment and other contextual attributes. Considering the temporal dependencies and compounding effects of various factors on growth trajectories, we formulate a probabilistic approach to the simulation task by solving a frame synthesis and pattern recognition problem. We introduce a sequence-informed plant growth simulation framework (SI-PGS) that employs a conditional generative model to implicitly learn a distribution of possible plant representations within a dynamic scene from a fusion of low-dimensional temporal sensor and context data. Methods such as controlled latent sampling and recurrent output connections are used to improve coherence in the plant structures between frames of prediction. In this work, we demonstrate that SI-PGS is able to capture temporal dependencies and continuously generate realistic frames of plant growth.