Aerial Visual Perception in Smart Farming : Field Study of Wheat Yellow Rust Monitoring
- Submitting institution
-
University of Aberdeen
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 171426402
- Type
- D - Journal article
- DOI
-
10.1109/TII.2020.2979237
- Title of journal
- IEEE Transactions on Industrial Informatics
- Article number
- -
- First page
- 2242
- Volume
- 17
- Issue
- 3
- ISSN
- 1551-3203
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2020
- 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
-
8
- Research group(s)
-
-
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- <Artificial intelligence>The research was carried out within a jointly-funded STFC and Newton Fund grant (ST/N006852/1). This is significant because wheat provides 20% of protein and food calories for 4.5B people and its disease monitoring is important in sustainable crop production and food security. This is the first time to integrate deep learning, UAV multispectral and RGB images to achieve wheat disease monitoring. This work is evaluated by field experiments along with generating an open-access dataset. This work has been followed on by USTC (World Computer Science Ranking: 61st) and Sheffield on their recent research on pest monitoring (https://tinyurl.com/yyaqezcv).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
