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

摘要： 在农业自动化采摘装备研发过程中，高精度识别、实时响应与轻量化设计成为目标检测算法需满足的核心指标。针对上述技术挑战，构建了轻量级苹果检测模型YEMB-FPN（YOLOv10n‑efficient multi‑branch FPN）。模型在主干网络（Backbone）使用分组高效多尺度卷积模块GEMS（Grouped efficient multi‑scale）代替原始YOLOv10n的主干网络最后2个C2f模块；在颈部网络（Neck）中，整体框架采用双向特征金字塔网络结构，使用加权双向特征金字塔融合模块（BiFPN_Fusion）替代原Neck的Concat，并创新设计跨阶段多尺度融合模块CS-MSFB（Cross‑stage multi‑scale fusion block）替代原Neck中的所有C2f和C2fCIB模块，完成模型改进。采用自建果园苹果目标检测数据集进行验证。结果表明，YEMB-FPN模型的mAP50达到98.6%，模型大小仅为4.6 MB，较基本模型YOLOv10n的mAP50提升3.3百分点，模型大小减少1.2 MB。表明该算法模型有效提升了苹果检测精度，轻量化设计显著增强了模型在低算力硬件平台的适配性。

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