YOLOv8-Ghost-QWA: A Lightweight Model with Quadro-Weighted Attention for Tomato Maturity Segmentation
Robust, real-time recognition of tomato maturity in unconstrained field conditions remains a critical bottleneck for autonomous harvesting platforms, as manual methods are costly and labor-intensive. Existing approaches, such as Vision Transformer-based models and YOLO variants with attention module...
I tiakina i:
| Ngā kaituhi matua: | , , , |
|---|---|
| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
Taylor & Francis Group
2026-12-01
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| Rangatū: | Applied Artificial Intelligence |
| Urunga tuihono: | https://www.tandfonline.com/doi/10.1080/08839514.2026.2684110 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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