智能化农业装备学报（中英文）

基于改进YOLOv8n的樱桃成熟度检测研究

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李瑞鑫 ¹ , 曹悦 ² , 司秀丽 ¹^,³ , 马丽 ²^,^*^,^*

智能化农业装备学报（中英文） | 2025,6(4): 40-49

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智能化农业装备学报（中英文） | 2025 , 6 (4) : 40 -49

基于改进YOLOv8n的樱桃成熟度检测研究

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李瑞鑫¹, 曹悦², 司秀丽¹^,³, 马丽²^,^*^,^*

作者信息

^1.吉林农业大学信息技术学院，吉林长春，130118

^2.无锡学院，江苏无锡，214105

^3.吉林省农业人工智能研究基地，吉林长春，130118

通讯作者:

^* 马丽，女，1979年生，吉林长春人，博士，教授；研究方向为智慧农业。E-mail：mali@cwxu.edu.cn

作者简介:

李瑞鑫，男，2000年生，山东滨州人，硕士研究生；研究方向为智慧农业。E-mail：liruixin0116@163.com

Research on cherry ripeness detection based on improved YOLOv8n

Ruixin LI¹, Yue CAO², Xiuli SI¹^,³, Li MA²

Affiliations

^1.School of Information Technology, Jilin Agricultural University, Changchun 130118, China

^2.Wuxi University, Wuxi 214105, China

^3.Jilin Agricultural Artificial Intelligence Research Base, Changchun 130118, China

Corresponding Author:

^* MA Li, E-mail: mali@cwxu.edu.cn

LI Ruixin, E-mail: liruixin0116@163.com

出版时间: 2025-11-15 doi: 10.12398/j.issn.2096-7217.2025.04.004

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摘要

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精确检测樱桃果实情况对提升果实品质、优化采摘时机及实现果园自动化管理具有重要意义。然而，由于樱桃果实体积小、颜色多样、背景复杂及重叠遮挡等因素，检测仍面临诸多挑战。为此，提出一种基于YOLOv8n架构的轻量化改进模型ACS-YOLOv8n，以提升检测精度与效率。该模型在主干网络中引入自适应下采样模块（ADown），融合平均池化与最大池化优势，结合多路径卷积操作，有效保留全局信息与局部显著特征，增强小目标检测能力。同时，采用Context Guided Block（CGB）替代传统C2f模块，通过局部、上下文与全局特征的联合提取，提升模型对目标与环境关系的感知能力与多尺度特征提取效果。在Neck部分，引入基于GSConv与VoV-GSCSP的Slim-Neck结构，优化特征融合方式，显著降低计算冗余，兼顾速度与精度。试验结果显示，ACS-YOLOv8n在复杂果园环境下的精度达92.94%，召回率90.66%，mAP50为96.57%；相较YOLOv8n，精度提升2.62个百分点，召回率提升1.57个百分点，平均精度提升2.08个百分点，同时模型参数量与计算量分别减少31.90%和33.33%，模型体积为原始模型的71.43%。该模型在复杂环境中表现出良好的检测效果与鲁棒性，为樱桃自动采摘与果园智能化管理提供了有效技术支撑。

关键词

樱桃 / 成熟度检测 / YOLOv8n / 多尺度特征提取 / 目标检测 / 深度学习

Abstract

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Accurate detection of cherry fruits is crucial for improving fruit quality, optimizing harvesting timing, and automating orchard management. However, due to factors such as small fruit size, diverse colors, complex backgrounds, and overlapping occlusions, detection still faced many challenges. To this end, this paper proposed a lightweight and improved model, ACS-YOLOv8n, based on the YOLOv8n architecture to enhance detection accuracy and efficiency. The model introduced an adaptive downsampling module (ADown) in the backbone network, which integrates the advantages of average pooling and max pooling, and combines multi-path convolution operations to effectively retain both global information and local salient features, thereby enhancing the detection capability of small targets. Meanwhile, the Context Guided Block (CGB) was used to replace the traditional C2f module, improving the model's ability to perceive the relationship between the target and the environment and enhancing the effect of multi-scale feature extraction through the joint extraction of local, contextual, and global features. In the neck part, the Slim-Neck structure based on GSConv and VoV-GSCSP was introduced to optimize the feature fusion method, significantly reduce computational redundancy, and balance speed and accuracy. The experimental results showed that ACS-YOLOv8n achieved 92.94% precision, 90.66% recall, and 96.57%mAP50 in complex orchard environments. Compared with YOLOv8n, the precision increased by 2.62 precentage point, the recall by 1.57 precentage point, and the average precision by 2.08 precentage point, while the number of model parameters and the computational volume were reduced by 31.90% and 33.33%, respectively, and the model size was 71.43% of the original model. The proposed model demonstrated good detection performance and robustness in complex environments, providing effective technical support for automatic cherry harvesting and intelligent orchard management.

Key words

cherry / ripeness detection / YOLOv8n / multi-scale feature extraction / object detection / deep learning

引用本文

李瑞鑫, 曹悦, 司秀丽, 马丽. 基于改进YOLOv8n的樱桃成熟度检测研究. 智能化农业装备学报（中英文）, 2025 , 6 (4) : 40 -49 . DOI: 10.12398/j.issn.2096-7217.2025.04.004

Ruixin LI, Yue CAO, Xiuli SI, Li MA. Research on cherry ripeness detection based on improved YOLOv8n[J]. Journal of Intelligent Agricultural Mechanization, 2025 , 6 (4) : 40 -49 . DOI: 10.12398/j.issn.2096-7217.2025.04.004

基金

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国家自然科学基金项目(U19A2061)
无锡学院科研启动项目(2024r007)

参考文献

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2025年第6卷第4期

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文章信息

doi: 10.12398/j.issn.2096-7217.2025.04.004

接收时间：2025-04-22
首发时间：2026-11-05
出版时间：2025-11-15

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出版历史

收稿日期：2025-04-22
修回日期：2025-06-11

基金

National Natural Science Foundation of China(U19A2061)

国家自然科学基金项目(U19A2061)

Wuxi University Research Start-up Project(2024r007)

无锡学院科研启动项目(2024r007)

作者信息

^1.吉林农业大学信息技术学院，吉林长春，130118

^2.无锡学院，江苏无锡，214105

^3.吉林省农业人工智能研究基地，吉林长春，130118

通讯作者:

^* 马丽，女，1979年生，吉林长春人，博士，教授；研究方向为智慧农业。E-mail：mali@cwxu.edu.cn

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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