Segmentation of Tuta Absoluta’s Damage on Tomato Plants: A Computer Vision Approach
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 instanc...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2021.1972254 |
| _version_ | 1797684834200453120 |
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| author | Loyani K. Loyani Karen Bradshaw Dina Machuve |
| author_facet | Loyani K. Loyani Karen Bradshaw Dina Machuve |
| author_sort | Loyani K. Loyani |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-12T00:36:30Z |
| format | Article |
| id | doaj.art-c0307b6bd6ab409ca3ea14203aa10e98 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:36:30Z |
| publishDate | 2021-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-c0307b6bd6ab409ca3ea14203aa10e982023-09-15T09:33:59ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452021-12-0135141107112710.1080/08839514.2021.19722541972254Segmentation of Tuta Absoluta’s Damage on Tomato Plants: A Computer Vision ApproachLoyani K. Loyani0Karen Bradshaw1Dina Machuve2The Nelson Mandela Institution of Science and TechnologyRhodes UniversityThe Nelson Mandela Institution of Science and TechnologyTuta 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.http://dx.doi.org/10.1080/08839514.2021.1972254 |
| spellingShingle | Loyani K. Loyani Karen Bradshaw Dina Machuve Segmentation of Tuta Absoluta’s Damage on Tomato Plants: A Computer Vision Approach Applied Artificial Intelligence |
| title | Segmentation of Tuta Absoluta’s Damage on Tomato Plants: A Computer Vision Approach |
| title_full | Segmentation of Tuta Absoluta’s Damage on Tomato Plants: A Computer Vision Approach |
| title_fullStr | Segmentation of Tuta Absoluta’s Damage on Tomato Plants: A Computer Vision Approach |
| title_full_unstemmed | Segmentation of Tuta Absoluta’s Damage on Tomato Plants: A Computer Vision Approach |
| title_short | Segmentation of Tuta Absoluta’s Damage on Tomato Plants: A Computer Vision Approach |
| title_sort | segmentation of tuta absoluta s damage on tomato plants a computer vision approach |
| url | http://dx.doi.org/10.1080/08839514.2021.1972254 |
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