Abstract: With the rapid development of modern agricultural technology, the precise management of crops and the effective control of weeds have become particularly important. Aiming at pigweed, a common weed that affects crop growth, an improved lightweight crop and pigweed detection algorithm based on YOLOv8, called MES-YOLO, is proposed. Firstly, MS-Block module and C2f module are fused and applied to the backbone network of the model by heterogeneous convolution, so as to improve the accuracy and efficiency of the overall target detection. Secondly, the feature pyramid structure HSFPN is improved to ELA-HSFPN and applied to the feature fusion network of the model to enhance the ability of the model to express the target features. Finally, the Inner-SIoU loss function is used to accelerate the convergence of the model. Experimental results demonstrate that, compared to YOLOv8n, the MES-YOLO detection algorithm achieves  2.1 percentage points  improvement in the mAP@0.5 metric, reduces computational complexity from 8.2×109 to 6.5×109, and has a parameter count that is only 62% of that of the YOLOv8n model. The improved model is more suitable for low-computational-power environments while meeting high-precision deployment requirements. 

Key words: deep learning, weed identification, heterogeneous convolution, feature pyramid structure, loss function