Development and Research of a Tomato Disease Detection Visual Platform Based on Light-ResNet50
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.
国家科技期刊平台
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