- Title
- Plant Disease Detection using Vision Transformers on Multispectral Natural Environment Images
- Creator
- De Silva, Malitha, Brown, Dane L
- Subject
- To be catalogued
- Date
- 2023
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/463456
- Identifier
- vital:76410
- Identifier
- xlink:href="https://ieeexplore.ieee.org/abstract/document/10220517"
- Description
- Enhancing agricultural practices has become essential in mitigating global hunger. Over the years, significant technological advancements have been introduced to improve the quality and quantity of harvests by effectively managing weeds, pests, and diseases. Many studies have focused on identifying plant diseases, as this information aids in making informed decisions about applying fungicides and fertilizers. Advanced systems often employ a combination of image processing and deep learning techniques to identify diseases based on visible symptoms. However, these systems typically rely on pre-existing datasets or images captured in controlled environments. This study showcases the efficacy of utilizing multispectral images captured in visible and Near Infrared (NIR) ranges for identifying plant diseases in real-world environmental conditions. The collected datasets were classified using popular Vision Transformer (ViT) models, including ViT- S16, ViT-BI6, ViT-LI6 and ViT-B32. The results showed impressive training and test accuracies for all the data collected using diverse Kolari vision lenses with 93.71 % and 90.02 %, respectively. This work highlights the potential of utilizing advanced imaging techniques for accurate and reliable plant disease identification in practical field conditions.
- Format
- computer, online resource, application/pdf, 1 online resource (5 pages), pdf
- Publisher
- IEEE Xplore
- Language
- English
- Relation
- 2023 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), De Silva, M. and Brown, D., 2023, August. Plant Disease Detection using Vision Transformers on Multispectral Natural Environment Images. In 2023 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) (pp. 1-6). IEEE, 2023 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) p. 1 2023
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of the IEEE Xplore Terms of Use Statement (https://ieeexplore.ieee.org/Xplorehelp/overview-of-ieee-xplore/terms-of-use)
- Rights
- Closed Access
- Hits: 88
- Visitors: 92
- Downloads: 7
Collections
SDG 15. Life On Land
Brown, Dane Lesley (Associate Prof)
SDG 02. Zero Hunger
SDG 14. Life Below Water
SDG 09. Industry, Innovation and Infrastructure
RU Department of Computer Science
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Plant Disease Detection using Vision Transformers on Multispectral Natural Environment Images.pdf | 675 KB | Adobe Acrobat PDF | View Details Download |