Fast estimation of plant growth dynamics using deep neural networks

Plant Methods

Alex Jordan¹  Talmo D. Pereira²  Yasmine Meroz³  Gabriella E. C. Gall⁴ 

[1] Centre for the Advanced Study of Collective Behaviour, Konstanz, Germany;Department of Collective Behaviour, Max Planck Institute of Animal Behavior, Konstanz, Germany;Princeton Neuroscience Institute, Princeton University, Princeton, USA;Computational Neurobiology Lab, Salk Institute for Biological Studies, La Jolla, USA;School of Plant Sciences and Food Security, Tel Aviv University, Tel Aviv, Israel;School of Plant Sciences and Food Security, Tel Aviv University, Tel Aviv, Israel;Zukunftskolleg, Department of Biology, University of Konstanz, Konstanz, Germany;Centre for the Advanced Study of Collective Behaviour, Konstanz, Germany;

关键词: Plant movement;    Tracking;    SLEAP;    Time-lapse imagery;    Pose estimation;    Deep learning;    Motion capture;   

DOI  :  10.1186/s13007-022-00851-9

来源: Springer

【 摘 要 】

BackgroundIn recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic movements include circumnutations, a circular movement of plant organs commonly associated with search and exploration, while tropisms refer to the directed growth of plant organs toward or away from environmental stimuli, such as light and gravity. Tracking these movements is therefore fundamental for the study of plant behaviour. Convolutional neural networks, as used for human and animal pose estimation, offer an interesting avenue for plant tracking. Here we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking. We evaluated it on time-lapse videos of cases spanning a variety of parameters, such as: (i) organ types and imaging angles (e.g., top-view crown leaves vs. side-view shoots and roots), (ii) lighting conditions (full spectrum vs. IR), (iii) plant morphologies and scales (100 μm-scale Arabidopsis seedlings vs. cm-scale sunflowers and beans), and (iv) movement types (circumnutations, tropisms and twining).ResultsOverall, we found SLEAP to be accurate in tracking side views of shoots and roots, requiring only a low number of user-labelled frames for training. Top views of plant crowns made up of multiple leaves were found to be more challenging, due to the changing 2D morphology of leaves, and the occlusions of overlapping leaves. This required a larger number of labelled frames, and the choice of labelling “skeleton” had great impact on prediction accuracy, i.e., a more complex skeleton with fewer individuals (tracking individual plants) provided better results than a simpler skeleton with more individuals (tracking individual leaves).ConclusionsIn all, these results suggest SLEAP is a robust and versatile tool for high-throughput automated tracking of plants, presenting a new avenue for research focusing on plant dynamics.

【 授权许可】

CC BY