内蒙古民族大学学报（自然科学版）2025，Vol.40Issue(2)：69-76,8.DOI:10.14045/j.cnki.15-1220.2025.02.010
基于改进YOLOv10n的玉米叶片病害检测研究
Research on Maize Leaf Disease Detection Based on Improved YOLOv10n
摘要
Abstract
Aiming at the problems of insufficient recognition accuracy and excessive parameter amount of the existing target detection models in the maize leaf disease detection task,an improved model for maize leaf disease detection based on YOLOv10n is proposed.By comparing the classical models such as YOLOv5n,YOLOv6n,YO-LOv8n,and YOLOv9t,it is found that YOLOv10n has the highest detection accuracy.The original dataset is expand-ed by using offline enhancement technology,which significantly improves the detection accuracy of the model.The introduction of the CBAM attention mechanism in the backbone network enhances the model's to focus on important features.Compared with the original YOLOv10n algorithm,the enhanced YOLOv10n algorithm's accuracy has in-creased by 7.3%,Recall has increased by 5.5%,and mAP@0.5 has increased by 10.9%.Compared with the original YOLOv10n,the accuracy of YOLOv10n-Enhance algorithm by adding CBAM attention mechanism has increased by 9.5%,and Recall has increased by 6.7%,with mAP@0.5 reaching 93.9%.The improved YOLOv10n algorithm signif-icantly improves the detection of maize leaf diseases without increasing the number of parameters and computational complexity.关键词
病害/YOLOv10n/注意力机制Key words
disease/YOLOv10n/attention mechanisms分类
农业科技引用本文复制引用
党珊珊,白明宇,路扬,潘春宇,赵晨雨,乔世成..基于改进YOLOv10n的玉米叶片病害检测研究[J].内蒙古民族大学学报（自然科学版）,2025,40(2):69-76,8.基金项目
国家自然科学基金项目(62162049) （62162049）
内蒙古自治区直属高校基本科研业务费项目(GXKY23Z015) （GXKY23Z015）
内蒙古民族大学博士科研启动基金项目(BS658) （BS658）
