Deep machine learning provides state-of-the-art performance in image-based plant phenotyping
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1323260
- Type
- D - Journal article
- DOI
-
10.1093/gigascience/gix083
- Title of journal
- GigaScience
- Article number
- gix083
- First page
- 1
- Volume
- 6
- Issue
- 10
- ISSN
- 2047-217X
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
11
- Research group(s)
-
-
- Citation count
- 88
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The measurement of structural and functional plant traits (phenotyping) is a vital tool that is used globally to improve plant resilience and yield. This work presents a cutting-edge technique offering superior performance to traditional computer vision approaches. It was the first application of deep learning in plant phenotyping, and many researchers have cited this work as motivation of their own use of deep learning in this field. The paper was crucial in securing a 3 year industrially funded project with Syngenta (Dr. Rob Lind).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
