- Title
- Detecting plant species in the field with deep learning and drone technology:
- Creator
- James, Katherine L, Bradshaw, Karen L
- Date
- 2020
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/160445
- Identifier
- vital:40446
- Identifier
- https://0-doi.org.wam.seals.ac.za/10.1111/2041-210X.13473
- Description
- Aerial drones are providing a new source of high‐resolution imagery for mapping of plant species of interest, amongst other applications. On‐board detection algorithms could open the door to allow for applications in which drones can intelligently interact with their environment. However, the majority of plant detection studies have focused on detection in post‐flight processed orthomosaics. Greater research into developing detection algorithms robust to real‐world variations in environmental conditions is necessary, such that they are suitable for deployment in the field under variable conditions. We outline the steps necessary to develop such a system, show by example how real‐world considerations can be addressed during model training and briefly illustrate the performance of our best performing model in the field when integrated with an aerial drone.
- Format
- 12 pages, pdf
- Language
- English
- Relation
- Methods in Ecology and Evolution, James, K. and Bradshaw, K., Detecting plant species in the field with deep learning and drone technology. Methods in Ecology and Evolution. 2020: 1-11, Methods in Ecology and Evolution volume 2020 number 1 11 February 2020 2041-210X
- Rights
- Publishers
- Rights
- Use of this resource is governed by the terms and conditions of the Wiley Library Online Terms of Use Statement (https://onlinelibrary.wiley.com/terms-and-conditions)
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Collections
Bradshaw, Karen Lee (Assoc Prof)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
SDG 15. Life On Land
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