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
林祺烨 1王增宇 1王润泽1
作者信息
- 1. 吉林农业大学,吉林 长春 130118
- 折叠
摘要
为了及时准确地识别和监测番茄病害,通过Flask框架开发一种基于改进Light-ResNet的番茄病害网页系统,系统使用预训练ResNet50 模型作为基础网络,通过添加注意力机制、深度可分离卷积实现了ResNet50 网络的轻量化改进及识别精度优化,并对其进行微调以适应番茄病害识别任务.最后通过将最终模型Light-ResNet50 与传统ResNet50 网络相对比,结果表明模型参数量缩减了 39.84%,最终精度为 97.27%,该系统具有更高的准确性和鲁棒性,为番茄生产提供了可靠的决策支持工具.
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引用本文复制引用
基金项目
吉林省大学生创新创业训练计划项目(S202310193051)
出版年
2024
国家科技期刊平台
NSTL
万方数据