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The aim is to enhance weed discrimination at the seedling stage in maize–soybean rotation systems through a novel detection method.

April 7, 2026PlantsOpen Access

Weed Discrimination at the Seedling Stage in Dryland Fields Under Maize–Soybean Rotation

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An intervention improves weed identification accuracy in dryland fields, indicating better precision agriculture management.

The aim is to enhance weed discrimination at the seedling stage in maize–soybean rotation systems through a novel detection method.
Developed an improved YOLOv11n model for weed detection
Incorporated Dynamic Convolution for better feature representation
Designed a lightweight SlimNeck architecture for efficient feature propagation
Integrated a cascaded group attention mechanism for enhanced background discrimination
Achieved mAP@0.5 of 87.2% for broadleaf weeds and 73.9% for Poaceae weeds
Consistent performance improvements over previous YOLO models
Demonstrated enhanced accuracy and robustness in UAV-acquired field images

Yue et al. (2026) studied this question.

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