DeepPod : A convolutional neural network based quantification of fruit number in Arabidopsis
- Submitting institution
-
Aberystwyth University / Prifysgol Aberystwyth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 38976296
- Type
- D - Journal article
- DOI
-
10.1093/gigascience/giaa012
- Title of journal
- GigaScience
- Article number
- -
- First page
- 1
- Volume
- 9
- Issue
- 3
- ISSN
- 2047-217X
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
https://academic.oup.com/gigascience/article/9/3/giaa012/5780255#supplementary-data
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
6
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Presents a novel deep learning based image quantification framework for rapid and accurate estimate of fruit numbers and sizes, using partially labelled data. The paper also offers the first large scale open source dataset (of 2400+ images) including manual fruit counting for a model plant widely used in real-world research on crop and ecological systems. Published in this prestigious journal, it forms a benchmark inviting the community for future data or model reuse in both research and practical applications. The work was funded by BBSRC and NSF, in collaboration with the National Plant Phenomics Centre and Oxford University.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -