Deep Learning Based Growth Modeling of Plant Phenotypes

Abstract

This paper investigates the less explored domain of imagebased plant growth modeling using deep learning. Our approach aims to model plant phenotypes at different growth stages, laying the foundation for further research on generating synthethic training data for object detection and segmentation We introduce a novel class of neural network architectures called Latent Plant Growth Models (LPGMs), which model the temporal aspects of plant growth within the latent space of pre-trained autoencoders. Two distinct LPGM architectures have been developed and investigated. These architectures demonstrate improved performance over existing state-of-the-art methods in terms of image quality and training time, while retaining a high degree of realism in the simulated growth.

Read More: TBA ECCV 2024 Proceedings

Cite As

@InProceedings{Hohl_2024_ECCV,
    author    = {Hohl, Renke and Schauer, Moritz and  Ghobadi, Seyed Eghbal},
    title     = {Deep Learning Based Growth Modeling of Plant Phenotypes},
    booktitle = {Computer Vision -- ECCV 2024 Workshops},
    month     = {September},
    year      = {2024},
    publisher = {Springer Nature Switzerland}
}