安徽农业科学2025，Vol.53Issue(6)：238-242,5.DOI:10.3969/j.issn.0517-6611.2025.06.053
基于改进YOLOX_Nano的番茄叶片病害识别研究
Research on Tomato Leaf Disease Recognition Based on Improved YOLOX_Nano
摘要
Abstract
In recent years,tomatoes have suffered from an increasing number of diseases,which have a huge impact on tomato yield and fruit quality.Timely and efficient identification of diseases and taking effective measures have become an urgent need for tomato production.In re-sponse to the low recognition rate of tomato diseases in existing models and the problem of large and complex models,this paper proposes a dis-ease recognition model based on improved YOLOX_Nano.By introducing a global attention mechanism to enhance the global information cap-ture ability of feature maps,improving the upsampling module in the feature pyramid network and the downsampling module in the path aggre-gation network,the expression ability and fusion effect of features can be improved.The experimental results showed that the mAP of this meth-od for identifying tomato leaf diseases reached 89.16％.The optimized model not only exhibits high accuracy and fast detection performance in tomato leaf disease recognition,but also has fewer parameters and calculations,making it easy to deploy on mobile devices such as smartpho-nes.This method can provide a reference for lightweight,fast,and efficient identification of tomato leaf diseases.关键词
YOLOX_Nano网络/GAM注意力机制/番茄病害识别Key words
YOLOX_Nano net/Global attention mechanism/Tomato disease identification分类
信息技术与安全科学引用本文复制引用
方晓捷,严李强,张福豪,宋沛琳..基于改进YOLOX_Nano的番茄叶片病害识别研究[J].安徽农业科学,2025,53(6):238-242,5.基金项目
2021年中央引导地方科技发展资金项目(XZ202101YD-0014C) （XZ202101YD-0014C）
西藏大学研究生"高水平人才培养计划"项目(2022-GSP-S106). （2022-GSP-S106）
