Segmentation-based quantification of Tuta absoluta’s damage on tomato plants
The invasion of the tomato leaf miner (Tuta absoluta) poses a significant threat to tomato productivity, leading to substantial yield losses for farmers. Currently, there is a lack of reliable methods for quantifying the effects of Tuta absoluta at an early stage before it causes significant damage....
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524000200 |
| Summary: | The invasion of the tomato leaf miner (Tuta absoluta) poses a significant threat to tomato productivity, leading to substantial yield losses for farmers. Currently, there is a lack of reliable methods for quantifying the effects of Tuta absoluta at an early stage before it causes significant damage. This research proposes a deep Convolutional Neural Network (CNN) model for the segmentation-based quantification of Tuta absoluta on tomato plants. The proposed quantification method employed a Mask RCNN model that achieved a mAP of 85.67 % and precisely detected and segmented the shapes of Tuta absoluta-infected areas on tomato leaves. The ability to accurately detect, segment and count Tuta mines in a tomato leaf image can have a significant impact on the agricultural industry by enabling farmers to quickly assess the extent of damage to their crops and take appropriate measures to prevent further losses. |
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| ISSN: | 2772-3755 |