Abstract: Aiming at  the problem of imbalance between recognition accuracy  and network  structure complexity, we proposed  a lightweight identification model Weed-YOLO for corn seedling weed based on improved YOLOv9. Firstly, FasterNet module was introduced as the backbone network of YOLOv9 model to effectively reduce the complexity of the model. Secondly, the bidirectional feature pyramid network was used to replace original path aggregation network and feature pyramid network modules to integrate 
multi-scale weed features, compensate for the decrease in recognition accuracy caused by lightweight backbone networks, and further reduce the complexity of the model. Finally, Inner_CIoU loss function was used to calculate the boundary frame loss, which improved the convergence speed and overall performance of the model. The experimental results show  that the accuracy, recall rate and mean precision of Weed-YOLO model in identifying corn seedling weeds are  94.1%, 95.9% and  97.6%, respectively.  The parameter count, amount of calculation and model size decrease by 39.15%,44.79% and 39.18%, respectively. It can accurately distinguish  weeds and crops, and reduce  resource utilization of the model.

Key words: weed, lightweight, YOLOv9 model, FasterNet module, bidirectional feature pyramid network, Inner_CIoU loss function, precision agriculture