智能化农业装备学报（中英文）2026，Vol.7Issue(3)：30-37,8.DOI:10.12398/j.issn.2096-7217.2026.03.003
基于改进YOLOv10n的马铃薯形状识别方法
A potato shape recognition method based on an improved YOLOv10n
摘要
Abstract
To improve the recognition accuracy for normal and deformed potatoes and enhance the model's ability to extract irregular contours,local protrusions,and complex boundary features,this study proposes an improved YOLOv10n-based potato shape recognition method integrating GCSA,MDFA,and MECS modules.Normal potatoes and typical deformed potatoes were used as the research objects,and a machine vision image acquisition platform was used to construct two datasets:normal and deformity.On this basis,YOLOv8n,YOLOv10n,YOLO11n,and YOLO12n were selected for comparative experiments,and YOLOv10n was chosen as the baseline model.Subsequently,a global channel-spatial attention module,a multi-scale dilated feature aggregation module,and a median-enhanced spatial and channel attention module were incorporated into the YOLOv10n network to improve the model in terms of key region attention,multi-scale feature fusion,and complex boundary feature enhancement,respectively.Ablation experiments were conducted to verify the model performance.The dataset annotation results showed that there were 2 175 deformed potato samples and 738 normal potato samples.The comparison results of the baseline models showed that the maximum precision,recall,and mAP@0.5 of YOLOv10n reached 91.500%,90.990%,and 90.980%,respectively,outperforming the other comparison models.The ablation experiment results showed that the YOLOv10n+GCSA+MDFA+MECS model achieved the best recognition performance,with precision,recall,and mAP@0.5 reaching 97.823%,96.835%,and 96.890%,respectively.Compared with the original YOLOv10n,the mAP@0.5 increased by 5.910 percentage points.The improved YOLOv10n model can effectively enhance the representation ability of abnormal contours,local protrusions,and multi-scale morphological features of potatoes,improve the recognition performance of normal and deformed potatoes,and provide an algorithmic basis for intelligent potato sorting and automatic recognition of deformed potatoes.关键词
马铃薯/形状识别/机器视觉/YOLOv10n/注意力机制/智能分选Key words
potato/shape recognition/machine vision/YOLOv10n/attention mechanism/intelligent sorting分类
农业科技引用本文复制引用
徐秋颖,汪迁,范文浩,王文渊,邓伟刚..基于改进YOLOv10n的马铃薯形状识别方法[J].智能化农业装备学报（中英文）,2026,7(3):30-37,8.基金项目
内蒙古农业大学高层次/优秀博士引进人才科研启动项目(NDYB2021-12) （NDYB2021-12）
内蒙古自治区自然科学基金(2026MS0805) （2026MS0805）
内蒙古自治区一流学科科研专项项目(YLXKZX-NND-047) （YLXKZX-NND-047）
国家自然科学基金(52565026) Scientific Research Start-up Foundation for High-level/Outstanding Doctoral Talents of Inner Mongolia Agricultural University(NDYB2021-12) （52565026）
Natural Science Foundation of Inner Mongolia Autonomous Region of China(2026MS0805) （2026MS0805）
Inner Mongolia Autonomous Region First-Class Discipline Research Special Project(YLXKZX-NND-047) （YLXKZX-NND-047）
National Natural Science Foundation of China(52565026) （52565026）
