- Title
- Segmentation of Tuta Absoluta’s Damage on Tomato Plants: A Computer Vision Approach
- Creator
- Loyani, Loyani K, Bradshaw, Karen L, Machuze, Dina
- Subject
- To be catalogued
- Date
- 2021
- Type
- text
- Type
- article
- Identifier
- http://hdl.handle.net/10962/440313
- Identifier
- vital:73765
- Identifier
- xlink:href="https://doi.org/10.1080/08839514.2021.1972254"
- Description
- Tuta absoluta is a major threat to tomato production, causing losses ranging from 80% to 100% when not properly managed. Early detection of T. absoluta’s effects on tomato plants is important in controlling and preventing severe pest damage on tomatoes. In this study, we propose semantic and instance segmentation models based on U-Net and Mask RCNN, deep Convolutional Neural Networks (CNN) to segment the effects of T. absoluta on tomato leaf images at pixel level using field data. The results show that Mask RCNN achieved a mean Average Precision of 85.67%, while the U-Net model achieved an Intersection over Union of 78.60% and Dice coefficient of 82.86%. Both models can precisely generate segmentations indicating the exact spots/areas infested by T. absoluta in tomato leaves. The model will help farmers and extension officers make informed decisions to improve tomato productivity and rescue farmers from annual losses.
- Format
- computer, online resource, application/pdf, 1 online resource (21 pages), pdf
- Publisher
- Taylor and Francis
- Language
- English
- Relation
- Applied Artificial Intelligence, Loyani, L. K., Bradshaw, K. and Machuve, D. (2021) ‘Segmentation of Tuta Absoluta’s Damage on Tomato Plants: A Computer Vision Approach’, Applied Artificial Intelligence, 35(14), pp. 1107–1127, Applied Artificial Intelligence volume 35 number 14 p. 1107 2021 1087-6545
- Rights
- Publisher
- Rights
- This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
- Rights
- Open Access
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Collections
RU Department of Computer Science
Bradshaw, Karen Lee (Assoc Prof)
SDG 09. Industry, Innovation and Infrastructure
SDG 15. Life On Land
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details | SOURCE1 | Segmentation of Tuta Absoluta s Damage on Tomato Plants A Computer Vision Approach.pdf | 7 MB | Adobe Acrobat PDF | View Details |