- Title
- Plant disease detection using deep learning on natural environment images
- Creator
- De Silva, Malitha, Brown, Dane L
- Subject
- To be catalogued
- Date
- 2022
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/465212
- Identifier
- vital:76583
- Identifier
- xlink:href="https://ieeexplore.ieee.org/abstract/document/9855925"
- Description
- Improving agriculture is one of the major concerns today, as it helps reduce global hunger. In past years, many technological advancements have been introduced to enhance harvest quality and quantity by controlling and preventing weeds, pests, and diseases. Several studies have focused on identifying diseases in plants, as it helps to make decisions on spraying fungicides and fertilizers. State-of-the-art systems typically combine image processing and deep learning methods to identify conditions with visible symptoms. However, they use already available data sets or images taken in controlled environments. This study was conducted on two data sets of ten plants collected in a natural environment. The first dataset contained RGB Visible images, while the second contained Near-Infrared (NIR) images of healthy and diseased leaves. The visible image dataset showed higher training and validation accuracies than the NIR image dataset with ResNet, Inception, VGG and MobileNet architectures. For the visible image and NIR dataset, ResNet-50V2 outperformed other models with validation accuracies of 98.35% and 94.01%, respectively.
- Format
- computer, online resource, application/pdf, 1 online resource (4 pages), pdf
- Publisher
- IEEE Xplore
- Language
- English
- Relation
- 2022 international conference on artificial intelligence, big data, computing and data communication systems (icABCD), De Silva, M. and Brown, D., 2022, August. Plant disease detection using deep learning on natural environment images. In 2022 international conference on artificial intelligence, big data, computing and data communication systems (icABCD) (pp. 1-5). IEEE, 2022 international conference on artificial intelligence, big data, computing and data communication systems (icABCD) p. 1 2022
- 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: 19
- Visitors: 19
- Downloads: 0
Collections
Brown, Dane A (Dr)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
SDG 15. Life On Land
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details | SOURCE1 | Plant disease detection using deep learning on natural environment images.pdf | 649 KB | Adobe Acrobat PDF | View Details |