YOLOv8 yields unsatisfactory detection results

The gap

Evidence profile

Sourced from the stated research gap and synthesized of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 3 representative gaps

• YOLOV8-KF: A FRAMEWORK FOR PEST AND DISEASE DETECTION AND FRUIT-VEGETABLE RECOGNITION IN COMPLEX SCENARIOS (2026) · World Journal of Information Technology  · doi

  YOLOv8 yields unsatisfactory detection results under scenarios involving leaf occlusion. Traditional detection algorithms easily lead to missed detection and false detection due to small size and occlusion of targets.

  generalstated research gapevidence 5/5
  Keywords: yolov8 yields unsatisfactory detection results scenarios involving leaf
• Robust apple leaf disease diagnosis for sustainable horticulture: overcoming background noise with a multilayer transformer-based approach (2026) · Scientific Reports · doi

  Vision Transformer architectures are evaluated for disease classification (paper 2) and general plant disease detection (paper 8) but never jointly with YOLO-based detection in a unified framework for offline edge deployment, nor combined with severity scoring.

  generalsynthesizedevidence 5/5
  Keywords: vision transformer architectures evaluated disease classification paper general
• TDD-YOLO: A novel model for precise detection of tomato diseases (2026) · PLOS One · doi

  Across this set, YOLO-based detection is applied to tomato, citrus, cotton pests, and potato diseases, but no study combines YOLO with Vision Transformer in a hybrid architecture for disease detection, nor integrates such a hybrid with severity-priority scoring for offline edge deployment.

  generalsynthesizedevidence 5/5
  Keywords: across set yolo-based detection applied tomato citrus cotton

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