Abstract
INTRODUCTION: The deployment of robots for automated weeding holds significant promise in promoting sustainable agriculture and reducing labor requirements, with vision based detection being crucial for accurate weed identification. However, weed detection through computer vision presents various challenges, such as morphological similarities between weeds and crops, large-scale variations, occlusions, and the small size of the target objects. METHODS: To overcome these challenges, this paper proposes a novel object detection model, PD-YOLO, based on multi-scale feature fusion. Building on the YOLOv8n framework, the model introduces a Parallel Focusing Feature Pyramid (PF-FPN), which incorporates two key components: the Feature Filtering and Aggregation Module (FFAM) and the Hierarchical Adaptive Recalibration Fusion Module (HARFM). These modules facilitate efficient feature fusion both laterally and radially across the network. Furthermore, the inclusion of a dynamic detection head (Dyhead) significantly enhances the model's capacity to detect and locate weeds in complex environments. RESULTS AND DISCUSSION: Experimental results on two public weed datasets demonstrate the superior performance of PD-YOLO over state-the-art models, with a modest increase in computational cost. PD-YOLO improves the mean average precision (mAP) by 1.7% and 1.8% on the CottonWeedDet12 dataset at thresholds of 0.5 and 0.5-0.95, respectively. This research not only presents an efficient and accurate weed detection method but also offers new insights and technological advances for automated weed detection in agriculture.