Ghost-Attention-YOLOv8: Enhancing Rice Leaf Disease Detection with Lightweight Feature Extraction and Advanced Attention Mechanisms
In agricultural research, effective and efficient disease detection in crops is crucial for enhancing yield and sustainability. This study presents a novel approach to improving YOLOv8, a state-of-the-art object detection model, by integrating the Ghost model with three advanced attention mechanisms...
Saved in:
| Main Authors: | Thanh Dang Bui, Tra My Do Le |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | AgriEngineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2624-7402/7/4/93 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8
by: Qiang Hu, et al.
Published: (2025-04-01) -
Improved YOLOv8 Algorithm was Used to Segment Cucumber Seedlings Under Complex Artificial Light Conditions
by: Duokuo Zhang, et al.
Published: (2025-01-01) -
YOLOv8n-GBE: A Hybrid YOLOv8n Model With Ghost Convolutions and BiFPN-ECA Attention for Solar PV Defect Localization
by: Likitha Reddy Yeddula, et al.
Published: (2025-01-01) -
EcoDetect-YOLOv2: A High-Performance Model for Multi-Scale Waste Detection in Complex Surveillance Environments
by: Jing Su, et al.
Published: (2025-05-01) -
YOLOv8 Architectural Scene Section Recognition Method Based on SimAM-EMA Hybrid Attention Mechanism
by: Jiangang Ye, et al.
Published: (2025-05-01)