- Title
- Plant disease detection using multispectral imaging
- Creator
- De Silva, Malitha, Brown, Dane L
- Subject
- To be catalogued
- Date
- 2022
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/463439
- Identifier
- vital:76409
- Identifier
- xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-35641-4_24"
- Description
- People worldwide are undergoing many challenges, including food scarcity. Many pieces of research are now focused on improving agriculture to increase the harvest and reduce the cost. Identifying plant diseases and pests in the early stages helps to enhance the yield and reduce costs. However, most plant disease identification research with computer vision has been done with images taken in controlled environments on publically available data sets. Near-Infrared (NIR) imaging is a favourable approach for identifying plant diseases. Therefore, this study collected NIR images of healthy and diseased leaves in the natural environment. The dataset is tested with eight Convolutional Neural Network (CNN) models with different train-test splits ranging from 10:90 to 90:10. The evaluated models attained their highest training and test accuracies from the 70:30 split onwards. Xception outperformed all the other models in all train-test splits and achieved 100% accuracy, precision and recall in the 80:20 train-test split.
- Format
- computer, online resource, application/pdf, 1 online resource (18 pages), pdf
- Publisher
- SpringerLink
- Language
- English
- Relation
- International Advanced Computing Conference, De Silva, M. and Brown, D., 2022, December. Plant disease detection using multispectral imaging. In International Advanced Computing Conference (pp. 290-308). Cham: Springer Nature Switzerland, International Advanced Computing Conference p. 290 2022 1865-0937
- Rights
- Publisher
- Rights
- Use of this resource is governed by the terms and conditions of the SpringerLink Terms of Use Statement ( https://link.springer.com/termsandconditions)
- Rights
- Closed Access
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Collections
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Brown, Dane A (Dr)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details | SOURCE1 | Plant disease detection using multispectral imaging.pdf | 706 KB | Adobe Acrobat PDF | View Details |