What question did this study set out to answer?

To develop a lightweight and enhanced model for accurate weed detection in cotton fields using advanced techniques.

April 11, 2026AgricultureOpen Access

AVGS-YOLO: A Quad-Synergistic Lightweight Enhanced YOLOv11 Model for Accurate Cotton Weed Detection in Complex Field Environments

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The model demonstrates precise weed detection in cotton fields, indicating potential for efficient agricultural practices.

To develop a lightweight and enhanced model for accurate weed detection in cotton fields using advanced techniques.
Proposed AVGS-YOLO model utilizing Quadruple Synergistic Lightweight Perception Mechanism (QSLPM)
Integrated Slimneck for optimized feature extraction pathways
Replaced Conv modules with ADown for reducing parameter redundancy
Implemented SEAM for enhancing small object recognition
Used SIoU for improved bounding box localization accuracy
Achieved 95.9% precision and 94.2% recall in weed detection
Obtained 98.2% mAP50 and 93.3% mAP50-95 metrics
Reduced model parameters by 17.4% and GFLOPs by 27%
Achieved 14.5% reduction in model size while maintaining performance

Wang et al. (2026) studied this question.

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