- Title
- Investigating popular CNN architectures for plant disease detection
- Creator
- Poole, Louise C, Brown, Dane L
- Subject
- To be catalogued
- Date
- 2021
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/465168
- Identifier
- vital:76579
- Identifier
- xlink:href="https://ieeexplore.ieee.org/abstract/document/9519341"
- Description
- Food production and food security have become increasingly important due to climate change and rising population numbers. One method to prevent crop loss is to develop a system to allow for early, efficient and accurate identification of plant diseases. CNNs often outperform previously popular machine learning algorithms. There are many existing CNN architectures. We compared and analysed the popular state-of-the-art architectures, namely ResNet, GoogLeNet and VGG, when trained for plant disease classification. We found that ResNet performed the best on the balanced Mendeley Leaves and PlantVillage datasets, obtaining 91.95% and 95.80% accuracy respectively. However, the ResNet architecture was relatively computationally expensive and slow to train. GoogLeNet obtained accuracies very close to those of ResNet with 89.35% and 94.59% achieved on the Mendeley Leaves and PlantVillage datasets respectively and could be considered a less computationally expensive alternative.
- Format
- computer, online resource, application/pdf, 1 online resource (4 pages), pdf
- Publisher
- IEEE Xplore
- Language
- English
- Relation
- 2021 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Poole, L. and Brown, D., 2021, August. Investigating popular cnn architectures for plant disease detection. In 2021 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) (pp. 1-5). IEEE, 2021 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) p. 1 2021
- 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
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- Visitors: 37
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Collections
Brown, Dane A (Dr)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Investigating popular cnn architectures for plant disease detection.pdf | 645 KB | Adobe Acrobat PDF | View Details Download |