TL;DR
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.
Accurate and efficient rice-pest detection is essential for field scouting, early warning and precision control. This study presents a lightweight YOLOv11n-based framework for accurate edge deployment. It integrates a Multi-cognitive Visual Adapter (Mona), a Detail-Preserving Contextual Fusion module (DPCF) and Wise-Io...
To solve the problems in accurately and efficiently detecting weeds in rapeseed fields under complex conditions, this paper proposes an improved MMB-YOLO model based on YOLOv13n. This model is built upon YOLOv13n and employs the MobileNetV3 lightweight architecture as the backbone network to decrease the model comple...
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 t...
The proposed model provides a favorable balance between detection accuracy and computational efficiency, indicating its potential for real-time orchard perception on resource-constrained platforms.
The cotton field weed detection model must balance detection accuracy, model size, and inference efficiency when deployed at the edge. This study constructed a dataset comprising four types of weeds based on field images collected from cotton fields in Xinjiang and proposed a lightweight detection model, RGC-YOLO, base...
An intelligent pest detection framework based on EfficientNet and Feature Pyramid Network for fast and accurate field pest identification and a lightweight, reliable, and automated monitoring solution for field pest surveillance, thereby facilitating data-driven, precise pest management and advancing the practice of su...
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