Aug 2026· Journal of Real-Time Image Processing· Vol 23· 0 citations· 38 references
TL;DR
A YOLOv11-based lightweight weed detection model, termed multi-level feature aggregation-based YOLO (MFA-YOLO), which combines a gated progressive feature fusion (GPFF) module to enhance hierarchical feature interaction via learnable weighting and channel-wise gating, and an efficient feature aggregation (EFA) module that strengthens fine-grained texture and semantic representation through lightweight attention-guided aggregation.
As a major staple crop with global significance, maize is highly vulnerable to pest infestations throughout its growth cycle, which can substantially limit plant development and reduce yield. In practice, accurate detection remains challenging due to pronounced morphological variations across pest life stages, as well...
This work proposes Enhance-YOLOv8, which replaces YOLOv8's Cross Stage Partial with 2 convolutions (C2f) backbone module with Enhance Adaptive Fine-grained Channel Attention (Enhance_AFCA), which integrates hierarchical multi-scale feature extraction and adaptive edge enhancement to address edge information loss and in...
Saiqi Pi, Fa-Yuan Xu, Fei Wang et al.· PeerJ Computer Science· 0 citations
Results demonstrate that DCG-YOLO provides a favorable trade-off between detection accuracy and computational cost, indicating its potential for future real-time weed detection on agricultural edge platforms.
Ping Yu, Xiaoqing Xu, Hui Yan et al.· Journal of Crop Health· 0 citations
Weed interference is a major constraint in the production of Angelica dahurica, where seedling–weed similarity, small target size, soil background variation, and occlusion make reliable field recognition difficult. This study develops a lightweight attention-enhanced one-stage detector for real-time crop-versus-weed lo...
Jing Pang, Xing-Yang Yang, Ze-Kun Ge et al.· Agronomy· 0 citations
Introduction In precision agriculture, accurate and efficient detection of crop pests and diseases is crucial. However, existing models in complex environments are prone to insufficient spatial perception and attenuation of disease texture features, making it difficult to balance recognition accuracy and lightweighting...
Wen-Bo Ma, Hao Sun, Kun Zhou et al.· Frontiers in Plant Science· 0 citations
Variable illumination, severe occlusion, and the small scale of pest instances in complex forest environments significantly compromise the accuracy of automated forest pest detection. Existing deep learning-based detection models often face challenges in achieving an optimal trade-off among detection accuracy, model li...
