A Lightweight Model for Shine Muscat Grape Detection in Complex Environments Based on the YOLOv8 Architecture
Automated harvesting of “Sunshine Rose” grapes requires accurate detection and classification of grape clusters under challenging orchard conditions, such as occlusion and variable lighting, while ensuring that the model can be deployed on resource- and computation-constrained edge devices. This stu...
Saved in:
Main Authors: | Changlei Tian, Zhanchong Liu, Haosen Chen, Fanglong Dong, Xiaoxiang Liu, Cong Lin |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Agronomy |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-4395/15/1/174 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Grape Target Detection Method in Orchard Environment Based on Improved YOLOv7
by: Fuchun Sun, et al.
Published: (2024-12-01) -
A lightweight wheat ear counting model in UAV images based on improved YOLOv8
by: Ruofan Li, et al.
Published: (2025-02-01) -
YOLOv8s-Longan: a lightweight detection method for the longan fruit-picking UAV
by: Jun Li, et al.
Published: (2025-01-01) -
Research on the lightweight detection method of rail internal damage based on improved YOLOv8
by: Xiaochun Wu, et al.
Published: (2025-01-01) -
Vehicle Detection and Tracking Based on Improved YOLOv8
by: Yunxiang Liu, et al.
Published: (2025-01-01)