To address the limitations of conventional target-detection algorithms in accurately handling multi-scale targets, this paper proposes an improved algorithm based on the YOLOv8 network. The capability of the model to extract multi-scale features is enhanced by incorporating an adaptive feature pyramid network (AFPN), enabling better adaptability and robustness in identifying targets of varying sizes. The incorporation of data augmentation techniques and optimized training strategies further improves the generalization capability of the model. Experiments performed on multiple public datasets demonstrate that the proposed algorithm significantly outperforms faster region-based convolutional neural network (Faster R-CNN), RetinaNet, and the original YOLOv8 architecture in terms of detection accuracy. Furthermore, experiments on a self-made mango dataset demonstrate the outstanding performance of the proposed method in recognizing multi-scale targets. The proposed algorithm not only affords insights into optimizing object-detection algorithms but also provides an effective reference for multi-scale target-detection tasks in agriculture and other fields.