Edge-Guided DETR Model for Intelligent Sensing of Tomato Ripeness Under Complex Environments
Tomato ripeness detection in open-field environments is challenged by dense planting, heavy occlusion, and complex lighting conditions. Existing methods mainly rely on color and texture cues, limiting boundary perception and causing redundant predictions in crowded scenes. To address these issues, w...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/13/2095 |
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| Summary: | Tomato ripeness detection in open-field environments is challenged by dense planting, heavy occlusion, and complex lighting conditions. Existing methods mainly rely on color and texture cues, limiting boundary perception and causing redundant predictions in crowded scenes. To address these issues, we propose an improved detection framework called Edge-Guided DETR (EG-DETR), based on the DEtection TRansformer (DETR). EG-DETR introduces edge prior information by extracting multi-scale edge features through an edge backbone network. These features are fused in the transformer decoder to guide queries toward foreground regions, which improves detection under occlusion. We further design a redundant box suppression strategy to reduce duplicate predictions caused by clustered fruits. We evaluated our method on a multimodal tomato dataset that included varied lighting conditions such as natural light, artificial light, low light, and sodium yellow light. Our experimental results show that EG-DETR achieves an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>P</mi></mrow></semantics></math></inline-formula> of 83.7% under challenging lighting and occlusion, outperforming existing models. This work provides a reliable intelligent sensing solution for automated harvesting in smart agriculture. |
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| ISSN: | 2227-7390 |