# Generative Plant Growth Simulation from Sequence-Informed Environmental Conditions

arXiv: https://arxiv.org/abs/2405.14796 · HTML: https://mlsanity.com/paper/2405.14796/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 23 May 2024.

## Builds on (most cited first, 5 of 5)

- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data](https://mlsanity.com/paper/1602.05875/) (2016)
- [Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2105.07789/) (2021)
- [Controlling Structured Output Representations from Attributes using Conditional Generative Models](https://mlsanity.com/paper/2305.00980/) (2023)

## Built on by (most cited first, 1 of 1)

- [Predictive Modeling, Pattern Recognition, and Spatiotemporal Representations of Plant Growth in Simulated and Controlled Environments: A Comprehensive Review](https://mlsanity.com/paper/2412.10538/) (2024)

## Abstract (arXiv)

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.