A deep learning approach to track Arabidopsis seedlings’ circumnutation from time-lapse videos

Plant Methods

Methodology

Eric D. Brenner¹  Hejian Liu²  Yixiang Mao²  Yao Wang² 

[1] Biology Department, Pace University, New York, NY, USA;Department of Electrical and Computer Engineering, New York University, Brooklyn, NY, USA;

关键词: Circumnutation;    Plant Movement Tracking;    Machine Learning;    Deep Learning;   

DOI  :  10.1186/s13007-023-00984-5

 received in 2022-07-12, accepted in 2023-01-17,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundCircumnutation (Darwin et al., SciRep 10(1):1–13, 2000) is the side-to-side movement common among growing plant appendages but the purpose of circumnutation is not always clear. Accurately tracking and quantifying circumnutation can help researchers to better study its underlying purpose.ResultsIn this paper, a deep learning-based model is proposed to track the circumnutating flowering apices in the plant Arabidopsis thaliana from time-lapse videos. By utilizing U-Net to segment the apex, and combining it with the model update mechanism, pre- and post- processing steps, the proposed model significantly improves the tracking time and accuracy over other baseline tracking methods. Additionally, we evaluate the computational complexity of the proposed model and further develop a method to accelerate the inference speed of the model. The fast algorithm can track the apices in real-time on a computer without a dedicated GPU.ConclusionWe demonstrate that the accuracy of tracking the flowering apices in the plant Arabidopsis thaliana can be improved with our proposed deep learning-based model in terms of both the racking success rate and the tracking error. We also show that the improvement in the tracking accuracy is statistically significant. The time-lapse video dataset of Arabidopsis is also provided which can be used for future studies on Arabidopsis in various takes.

【 授权许可】

CC BY   
© The Author(s) 2023

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