Res4net-CBAM: a deep cnn with convolution block attention module for tea leaf disease diagnosis

• 全文链接

Res4net CBAM：用于茶叶病害诊断的卷积块关注模块的深度卷积神经网络

关键词：

来源：

Multimedia Tools and Applications 期刊

全文链接：

//agri.nais.net.cn/topic/downloadFile/3f2e99f7-ef1d-4a9a-b933-abee5ba40b45

来源地址：

https://link.springer.com/article/10.1007/s11042-023-17472-6

资源所属：

茶学研究专题

类型：

学术文献

语种：

英语

原文发布日期：

2023-11-03

摘要：

In this study, we propose Res4net-CBAM, a deep convolutional neural network (CNN) specifically designed for tea leaf disease diagnosis, aiming to reduce the model’s complexity and improve disease identification accuracy. The Res4net-CBAM model utilizes a residual block-based Res4net architecture with a network interactive convolutional block attention module (CBAM) to accurately extract complex features associated with different diseases. We conducted extensive experiments to compare the performance of our model with standard CNN models such as AlexNet, VGG16, ResNet50, DenseNet121, and InceptionV3, based on metrics such as accuracy, precision, recall, and F1-score. Our results demonstrate that the Res4net-CBAM model outperforms all other models, achieving an average recognition accuracy of 98.27% on self-acquired tea leaf disease data samples. Specifically, the Res4net-CBAM model achieved an average sensitivity of 98.39%, specificity of 98.26%, precision of 98.35%, and F1-score of 98.37%, while utilizing the Adagrad optimizer with a learning rate of 0.001. Moreover, our model surpasses some recent and existing works in this field, highlighting its effectiveness in diagnosing tea leaf diseases.