YOLO-SW: A Real-Time Weed Detection Model for Soybean Fields Using Swin Transformer and RT-DETR

YOLO-SW: A Real-Time Weed Detection Model for Soybean Fields Using Swin Transformer and RT-DETR

Accurate weed detection in soybean fields is essential for enhancing crop yield and reducing herbicide usage. This study proposes a YOLO-SW model, an improved version of YOLOv8, to address the challenges of detecting weeds that are highly similar to the background in natural environments. The resear...

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Bibliographic Details
Main Authors: Yizhou Shuai, Jingsha Shi, Yi Li, Shaohao Zhou, Lihua Zhang, Jiong Mu
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Agronomy
Subjects:
weed detection
soybean weed
machine learning
smart agriculture
edge deployment
Online Access:https://www.mdpi.com/2073-4395/15/7/1712
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