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.
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Notes
- 1.
Code and supplementary material can be found at:
https://github.com/mohas95/Sequence-Informed-Plant-Growth-Simulation.
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Acknowledgements
This study was partially funded by Gardyn and Mitacs (IT16220). We thank the Gardyn team for providing the vertical growth systems essential for building our datasets. Special thanks to Anita Parmar, Ollivier Dyens, and the Building 21 members for their engagement in creative discussions.
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Debbagh, M., Liu, Y., Zheng, Z., Jiang, X., Sun, S., Lefsrud, M. (2024). Generative Plant Growth Simulation from Sequence-Informed Environmental Conditions. In: Suen, C.Y., Krzyzak, A., Ravanelli, M., Trentin, E., Subakan, C., Nobile, N. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2024. Lecture Notes in Computer Science(), vol 15154. Springer, Cham. https://doi.org/10.1007/978-3-031-71602-7_26
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