An Enhanced and Lightweight YOLOv8-Based Model for Accurate Rice Pest Detection
Accurate pest identification is crucial for ensuring both high quality and high yield in rice production. This paper proposes RicePest-YOLO, a practical and generalizable model designed for agricultural pest detection, based on structural optimization and lightweight strategies applied to the YOLOv8...
Saved in:
| Main Authors: | Guisuo Liu, Juxing Di, Qing Wang, Yan Zhao, Yang Yang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11003895/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An enhanced YOLOv8 model for accurate detection of solid floating waste
by: Juxing Di, et al.
Published: (2025-07-01) -
YOLOv8-DBW: An Improved YOLOv8-Based Algorithm for Maize Leaf Diseases and Pests Detection
by: Xiang Gan, et al.
Published: (2025-07-01) -
GhostConv+CA-YOLOv8n: a lightweight network for rice pest detection based on the aggregation of low-level features in real-world complex backgrounds
by: Fei Li, et al.
Published: (2025-08-01) -
Improving Rice Pest Management Through RP11: A Scientifically Annotated Dataset for Adult Insect Recognition
by: Biao Ding, et al.
Published: (2025-06-01) -
YOLOv8-MSP-PD: A Lightweight YOLOv8-Based Detection Method for Jinxiu Malus Fruit in Field Conditions
by: Yi Liu, et al.
Published: (2025-06-01)