The precise detection of grape clusters is a prerequisite for achieving yield estimation，picking and other operations， but existing methods are still difficult to achieve lightweight and accurate detection of multi.variety grape clusters. To enhance the accuracy，robustness，and generalization of multi.variety grape cluster detection in complex natural scenes，a model named ESIC-YOLOv8n is proposed based on the improved YOLOv8n model. In this model，EMA and SA attention modules are respectively added to the Backbone and Neck networks of YOLOv8n to strengthen the network′s feature extraction and multi.scale feature fusion capabilities，meanwhile，to reduce the interference from occlusion or overlap in grape cluster detection and to improve the detection accuracy and recall. In addition，by replacing CIoU with Inner CIoU in the head and using auxiliary boxes to improve the accuracy of overlapping object detection，the overall detection accuracy and generalization of the model was enhanced. As a result，the ESIC-YOLOv8n model achieves a detection accuracy of 87. 00%，a recall rate of 81. 60%，mAP of 88. 90%，and F1 score of 84. 21%，representing improvements of 1. 05%，2. 90%，1. 48% and 2. 00%，respectively，compared to the original YOLOv8n model. The results indicate that the ESIC-YOLOv8n model possesses high accuracy，good generalization，and lightweight characteristics，providing technical support for research on grape yield estimation and harvesting.

葡萄簇目标的精准检测是实现估产、采摘等作业的前提，现有方法难以实现多品种葡萄簇的轻量化精准检测。为提高复杂自然场景下多品种葡萄簇检测准确性、鲁棒性与泛化性，提出一种基于改进 YOLOv8n模型的多品种葡萄簇检测模型 ESIC-YOLOv8n，该模型在 YOLOv8n的 Backbone和 Neck网络中分别添加 EMA和 SA注意力模块，以加强网络的特征提取和多尺度特征融合能力，降低因遮挡或重叠对葡萄簇检测的干扰，提高检测精度和召回率；在 Head把 CIoU替换成 Inner-CIoU，利用辅助框提高重叠目标检测的准确性，从而提升模型整体的检测准确性和泛化性。ESIC-YOLOv8n模型的检测精度为 87. 00%，召回率为 81. 60%，mAP为 88. 90%，F1值为 84. 21%，较原 YOLOv8n模型分别提高1. 05%、2. 90%、1. 48%和2. 00%。结果表明，ESIC-YOLOv8n模型具有准确率高、泛化性好、轻量化等优点，可为葡萄产量估计、采摘等研究提供技术支持。