- Title
- Early Plant Disease Detection using Infrared and Mobile Photographs in Natural Environment
- Creator
- De Silva, Malitha
- Creator
- Brown, Dane L
- Subject
- To be catalogued
- Date Issued
- 2023
- Date
- 2023
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/464085
- Identifier
- vital:76474
- Identifier
- xlink:href="https://link.springer.com/chapter/10.1007/978-3-031-37717-4_21"
- Description
- Plant disease identification is a critical aspect of plant health management. Identifying plant diseases is challenging since they manifest themselves in various forms and tend to occur when the plant is still in its juvenile stage. Plant disease also has cascading effects on food security, livelihoods and the environment’s safety, so early detection is vital. This work demonstrates the effectiveness of mobile and multispectral images captured in viable and Near Infrared (NIR) ranges to identify plant diseases under realistic environmental conditions. The data sets were classified using popular CNN models Xception, DenseNet121 and ResNet50V2, resulting in greater than 92% training and 74% test accuracy for all the data collected using various Kolari vision lenses. Moreover, an openly available balanced data set was used to compare the effect of the data set balance and unbalanced characteristics on the classification accuracy. The result showed that balanced data sets do not impact the outcome.
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (14 pages)
- Format
- Publisher
- SpringerLink
- Language
- English
- Relation
- Science and Information Conference
- Relation
- De Silva, M. and Brown, D., 2023, July. Early Plant Disease Detection using Infrared and Mobile Photographs in Natural Environment. In Science and Information Conference (pp. 307-321). Cham: Springer Nature Switzerland
- Relation
- Science and Information Conference p. 307 2023 2367-3389
- 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
- Hits: 82
- Visitors: 83
- Downloads: 1
Collections
Brown, Dane A (Associate Prof)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
SDG 15. Life On Land
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Early Plant Disease Detection using Infrared and Mobile Photographs in Natural Environment.pdf | 708 KB | Adobe Acrobat PDF | View Details Download |