YOLOv8n-SSDW: A Lightweight and Accurate Model for Barnyard Grass Detection in Fields

Yan Sun, Hanrui Guo, Xiaoan Chen, Mengqi Li, Bing Fang and Yingli Cao ()
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Yan Sun: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Hanrui Guo: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Xiaoan Chen: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Mengqi Li: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Bing Fang: Department of Foreign Language Teaching, Shenyang Agricultural University, Shenyang 110866, China
Yingli Cao: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China

Agriculture, 2025, vol. 15, issue 14, 1-26

Abstract: Barnyard grass is a major noxious weed in paddy fields. Accurate and efficient identification of barnyard grass is crucial for precision field management. However, existing deep learning models generally suffer from high parameter counts and computational complexity, limiting their practical application in field scenarios. Moreover, the morphological similarity, overlapping, and occlusion between barnyard grass and rice pose challenges for reliable detection in complex environments. To address these issues, this study constructed a barnyard grass detection dataset using high-resolution images captured by a drone equipped with a high-definition camera in rice experimental fields in Haicheng City, Liaoning Province. A lightweight field barnyard grass detection model, YOLOv8n-SSDW, was proposed to enhance detection precision and speed. Based on the baseline YOLOv8n model, a novel Separable Residual Coord Conv (SRCConv) was designed to replace the original convolution module, significantly reducing parameters while maintaining detection accuracy. The Spatio-Channel Enhanced Attention Module (SEAM) was introduced and optimized to improve sensitivity to barnyard grass edge features. Additionally, the lightweight and efficient Dysample upsampling module was incorporated to enhance feature map resolution. A new WIoU loss function was developed to improve bounding box classification and regression accuracy. Comprehensive performance analysis demonstrated that YOLOv8n-SSDW outperformed state-of-the-art models. Ablation studies confirmed the effectiveness of each improvement module. The final fused model achieved lightweight performance while improving detection accuracy, with a 2.2% increase in mAP_50, 3.8% higher precision, 0.6% higher recall, 10.6% fewer parameters, 9.8% lower FLOPs, and an 11.1% reduction in model size compared to the baseline. Field tests using drones combined with ground-based computers further validated the model’s robustness in real-world complex paddy environments. The results indicate that YOLOv8n-SSDW exhibits excellent accuracy and efficiency. This study provides valuable insights for barnyard grass detection in rice fields.

Keywords: in-field barnyard grass detection; YOLOv8n-SSDW; lightweight model; deep learning; object detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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