Knowledge-based learning for plant phenotyping

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Knowledge-based learning for plant phenotyping
Title:
Knowledge-based learning for plant phenotyping
Journal Title:
10th International Conference on Functional- Structural Plant Models (FSPM2023)
DOI:
Publication Date:
27 March 2023
Citation:
L. Gobeawan, J. Chattoraj, F. Yang, C. W. Lim, X. Liu, and V. S. G. Raghavan, “Knowledge-based learning for plant phenotyping,” in 10th International Conference on Functional- Structural Plant Models (FSPM2023) (T.-W. Chen, A. F. K. Kahlen, and H. Sttzel, eds.), pp. 140–141, Mar 2023
Abstract:
Plant phenotyping, an effort to assess plant physiology traits such as growth, architecture or profile, and quantitative measurements, has been on a rising trend for its importance in understanding functioning and cultivation of plants for sustainable agriculture. The trend is mainly supported by emerging technologies in imaging and sensing, and then, artificial intelligence. Correspondingly, we attempt to automate the recognition of plant architecture from remote sensing LiDAR (laser imaging, detection, and ranging) data based on our related works in species modeling.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation, Singapore - Joint New Zealand - Singapore Data Science Research Programme
Grant Reference no. : SDSC-2020-002
Description:
ISSN:
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