关键词: 苹果, 目标检测, YOLOv10n, 特征金字塔网络, 多尺度卷积, 轻量化

Abstract: In the development of automated agricultural harvesting equipment，high‑precision recognition，real‑time response，and lightweight design are core requirements for target detection algorithms.To address these technical challenges，this study innovatively proposed a lightweight apple detection model named YEMB‑FPN（YOLOv10n‑efficient multi‑branch FPN）. The model replaced the last two C2f modules in the backbone network of the original YOLOv10n with the grouped efficient multi‑scale（GEMS） convolution module. In the neck network，a bidirectional feature pyramid framework was adopted，where the original Concat operation was replaced by a weighted bidirectional feature pyramid fusion module（BiFPN_Fusion）. Additionally，a novel cross‑stage multi‑scale fusion block（CS‑MSFB）was designed to substitute all original C2f and C2fCIB modules in the neck network.The model was validated using a self‑constructed apple target detection dataset from orchard environments.Experimental results demonstrated that the YEMB‑FPN model achieved an mAP50 of 98.6%，with a model size of only 4.6 MB.Compared to the baseline YOLOv10n，this represented a 3.3 percentage point improvement in mAP50 and a reduction of 1.2 MB in model size.These findings indicated that the proposed algorithm significantly enhanced apple detection accuracy，while its lightweight design markedly improved compatibility with low‑computational hardware platforms，which provided critical technical support for the embedded deployment of intelligent agricultural equipment.

Key words: Apple, Object detection, YOLOv10n, Feature pyramid network, Multi?scale convolution, Lightweight