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

Content Provider	Springer Nature : BioMed Central
Author	Mao, Yixiang Liu, Hejian Wang, Yao Brenner, Eric D.
Abstract	Background Circumnutation (Darwin et al., Sci Rep 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. Results In 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. Conclusion We 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.
Related Links	https://plantmethods.biomedcentral.com/counter/pdf/10.1186/s13007-023-00984-5.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	17464811
DOI	10.1186/s13007-023-00984-5
Journal	Plant Methods
Issue Number	1
Volume Number	19
Language	English
Publisher	BioMed Central
Publisher Date	2023-02-27
Access Restriction	Open
Subject Keyword	Plant Sciences Biological Techniques Circumnutation Plant Movement Tracking Machine Learning Deep Learning
Content Type	Text
Resource Type	Article
Subject	Plant Science Biotechnology Genetics
Journal Impact Factor	4.7/2023
5-Year Journal Impact Factor	5.6/2023

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