现代信息科技2024，Vol.8Issue(14)：49-53,58,6.DOI:10.19850/j.cnki.2096-4706.2024.14.010
基于Light-ResNet50的番茄病害检测可视化平台开发与研究
Development and Research of a Tomato Disease Detection Visual Platform Based on Light-ResNet50
摘要
Abstract
In order to timely and accurately identify and monitor tomato diseases,a tomato disease Web system based on improved Light-Res Net is developed using the Flask framework.The system uses a pre trained ResNet50 model as the basic network,and achieves lightweight improvement and recognition accuracy optimization of the ResNet50 network by adding Attention Mechanism and Depthwise Separable Convolutions.It is also fine tuned to adapt to the tomato disease recognition task.Finally,by comparing the final model Light-ResNet50 with the traditional ResNet50 network,the results show that the model parameter quantity is reduced by 39.84%,and the final accuracy is 97.27%.The system has higher accuracy and robustness,providing a reliable decision support tool for tomato production.关键词
ResNet/迁移学习/注意力机制/深度可分离卷积Key words
ResNet/Transfer Learning/Attention Mechanism/Depthwise Separable Convolution分类
信息技术与安全科学引用本文复制引用
林祺烨,王增宇,王润泽..基于Light-ResNet50的番茄病害检测可视化平台开发与研究[J].现代信息科技,2024,8(14):49-53,58,6.基金项目
吉林省大学生创新创业训练计划项目(S202310193051) （S202310193051）
