PestNet: An End-to-End Deep Learning Approach for Large-Scale Multi-Class Pest Detection and Classification
- Submitting institution
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Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1008
- Type
- D - Journal article
- DOI
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10.1109/ACCESS.2019.2909522
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 45301
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2019
- URL
-
-
- Supplementary information
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- 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
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6
- Research group(s)
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-
- Citation count
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Accurate pest detection is a major challenge in agriculture. This work is a result of a collaborative research project between LJMU, the University of Science and Technology of China, and Chinese Academy of Sciences, supported by the National Key Technology R&D Programme of China (2018YFD0200300) and the National Natural Science Foundation of China (31401293, 31671586, 61773360). One of the most significant contributions of the work is in the size of the dataset, covering more than 80k images with over 580k images of 16 different classes of pests labelled by agricultural experts.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -