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Predicting plant growth from time-series data using deep learning

Abstract:

Phenotyping involves the quantitative assessment of the anatomical, biochemical, and physiological plant traits. Natural plant growth cycles can be extremely slow, hindering the experimental processes of phenotyping. Deep learning offers a great deal of support for automating and addressing key plant phenotyping research issues. Machine learning-based high-throughput phenotyping is a potential solution to the phenotyping bottleneck, promising to accelerate the experimental cycles within ph...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.3390/rs13030331

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0002-1673-1946
Publisher:
MDPI
Journal:
Remote Sensing More from this journal
Volume:
13
Issue:
3
Article number:
331
Pages:
1-17
Publication date:
2021-02-01
Acceptance date:
2021-01-15
DOI:
EISSN:
2072-4292
Language:
English
Keywords:
Pubs id:
1162455
Local pid:
pubs:1162455
Deposit date:
2022-12-09

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