- 期刊
Maize Seedling and Weed Detection Using BFSL‐YOLOv8
摘要
Addressing the urgent need for weed control in maize seedlings due to the low detection accuracy in complex agricultural environments, this paper proposes a BFSL‐YOLOv8 model. paper proposes a BFSL‐YOLOv8 model. The model enhances multi‐scale feature extraction and fusion by integrating a Spatial Pyramid Pooling with Large Separable Kernel Attention and skip connections (SPPF_LSKA) module into the neck of YOLOv8 and efficiently captures long‐range dependencies by introducing an improved BiFormer module in the head. Experiments were conducted on a publicly available real‐world field monitoring dataset under Experiments were conducted on a publicly available real‐world field monitoring dataset under specific hardware and software environments, using stochastic gradient descent (SGD) for training. achieves a mean Average Precision (mAP) of 99.5% for maize detection and improves the mAP at an Intersection over Union (IoU) threshold of 0.5 (mAP50) for weed detection by 0.5 percentage points to 64.8% compared to the baseline YOLOv8. The model has 3.70M parameters and a processing time of 4.4ms per image. F1‐confidence curve analysis indicates that BFSL‐YOLOv8 exhibits good robustness in complex scenarios, with an optimal confidence threshold of 0.292 and an average confidence threshold of 1.5 times. of 0.292 and an average F1‐score of 0.82 for all classes. The experiments validate the effectiveness of the proposed method, providing a new solution for accurate and efficient weed detection in maize. accurate and efficient weed detection in maize fields for precision agriculture.