This study proposes a lightweight apple detection algorithm based on an improved YOLOv8n model for apple fruit recognition in natural orchard environments. Firstly, the study uses a combination of DSConv and FEM feature extraction modules to replace some regular convolutions in the backbone network for lightweight improvements. In this way, the floating-point numbers and computational quantity during the convolution process can be reduced. To maintain performance during the lightweight process, a structured state space model is introduced to construct the CBAMamba module, which efficiently processes features through the Mamba structure, during the feature processing procedure. Subsequently, the convolutions at the detecting head are replaced with RepConv and the convolution layer is reduced. Finally, the bounding box loss function is changed to the dynamic non-monotonic focusing mechanism WIoU to accelerate model convergence and further enhance model detection performance. The experiments show that, on the public dataset, the improved YOLOv8 algorithm outperforms the original YOLOv8n algorithm by 1.6% in mAP@0.5 and 1.2% in mAP@0.5:0.95. Meanwhile, it also increases FPS by 8.0% and reduces model parameters by 13.3%. The lightweight design makes it highly practical in robotics and embedded system deployment fields.