Multi-Scale Feature Enhancement for Robust Weed Detection in Complex Cotton Fields
Year: 2026
Type: article
Abstract: Precise weed detection in complex cotton fields is a critical prerequisite for site-specific weed control and precision management. To address the challenges of small weed targets, high inter-class similarity, occlusion, and illumination variations that affect detection accuracy in cotton fields, this paper proposes CWD-YOLO, an improved model based on YOLO26n. CWD-YOLO reconstructs the original backbone network using GhostNetV3 to enhance low-level visual feature extraction, integrates TSSA into the C2PSA module to strengthen high-level semantic feature representation, and adopts AssemFormer in the C3k2 feature fusion stage to improve multi-scale local–global feature fusion. These designs enhance the detection of small, weak-feature, and occluded weed targets. On the CottonWeedDet12 benchmark dataset, CWD-YOLO achieved Precision, Recall, [email protected], and [email protected]:0.95 values of 94.3%, 87.9%, 93.1%, and 87.6%, respectively, outperforming the baseline YOLO26n by 2.9, 2.7, 2.0, and 1.5 percentage points. Furthermore, in multi-dataset adaptability experiments on three external weed datasets, namely 3WDataset, 6WEED, and 3SeasonWeedDet10, CWD-YOLO consistently outperformed the baseline model, indicating its adaptability to different weed detection scenarios. The experimental results demonstrate that CWD-YOLO effectively improves weed detection accuracy in complex cotton fields and provides an effective method for precise field weed identification.
Source: Agronomy
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225783349
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