- Title
- Efficient Plant Disease Detection and Classification for Android
- Creator
- Brown, Dane L
- Creator
- Mazibuko, Sifisokuhle
- Subject
- To be catalogued
- Date Issued
- 2023
- Date
- 2023
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/464096
- Identifier
- vital:76475
- Identifier
- xlink:href="https://link.springer.com/chapter/10.1007/978-981-99-1624-5_39"
- Description
- This paper investigates the feasibility of using a CNN model to diagnose plant diseases in the wild. Plant diseases are a major risk to ecosystems, human and animal health, and the quality of life overall. They may reduce farm productivity drastically, leaving farmers with financial losses and food insecurity. Small-scale farmers and producers cannot pay for an expert to look at their plants for plant diseases because it would cost too much. A mobile solution is thus built for the Android platform that utilises a unified deep learning model to diagnose plant diseases and provide farmers with treatment information. The literature-recommended CNN architectures were first analysed on the PlantVillage dataset, and the best-performing model was trained for integration into the application. While training on the tomato subset of the PlantVillage dataset, the VGG16 and InceptionV3 networks achieved a higher F1-score of 94.49% than the MobileNetsV3Large and EfficientNetB0 networks (without parameter tuning). The VGG model achieved 94.43% accuracy and 0.24 loss on the RGB PlantVillage dataset, outperforming the segmented and greyscaled datasets, and was therefore chosen for use in the application. When tested on complex data collected in the wild, the VGG16 model trained on the RGB dataset yielded an accuracy of 63.02%. Thus, this research revealed the discrepancy between simple and real-world data, as well as the viability of present methodologies for future research.
- Format
- computer
- Format
- online resource
- Format
- application/pdf
- Format
- 1 online resource (14 pages)
- Format
- Publisher
- SpringerLink
- Language
- English
- Relation
- Inventive Systems and Control: Proceedings of ICISC 2023
- Relation
- Brown, D. and Mazibuko, S., 2023. Efficient Plant Disease Detection and Classification for Android. In Inventive Systems and Control: Proceedings of ICISC 2023 (pp. 535-549). Singapore: Springer Nature Singapore
- Relation
- Inventive Systems and Control: Proceedings of ICISC 2023 p. 535 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
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- Visitors: 84
- Downloads: 1
Collections
Brown, Dane A (Associate Prof)
RU Department of Computer Science
SDG 10. Reduced Inequalities
SDG 09. Industry, Innovation and Infrastructure
SDG 15. Life On Land
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Efficient Plant Disease Detection and Classification for Android.pdf | 711 KB | Adobe Acrobat PDF | View Details Download |