Abstract: To address the issues of low accuracy and missed or false detection technology for fruits and vegetables, an improved YOLOv8 algorithm named YOLOv8-GFPN is proposed. The GFPN network was used to replace the original YOLOv8 Neck network to enhance the model’s feature extraction ability. C2f-fast-EMA was employed to replace the original C2f module, reducing model parameters and computational complexity. Wise-IoU was introduced to replace the original CIoU loss function, improving overall model performance. To verify the advancement of improved YOLOv8-GFPN algorithm, the improved algorithm was compared with original YOLOv8 on fruit and vegetable disease dataset. mAP@0.5 increased by 2.9 percentage points, mAP@0.5:0.95 increased by 5.1 percentage points, GFLOPs reduced by 17%. Experimental results show that the improved algorithm is more suitable for fruit and vegetable disease recognition.