RIC-Net: A plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism
2022
Zhao, Yun | Sun, Cheng | Xu, Xing | Chen, Jiagui
In this paper, we proposed a convolutional neural network based on Inception and residual structure with an embedded modified convolutional block attention module (CBAM), aiming to improve the classification of plant leaf diseases. Corn, potatoes and tomatoes are the most cultivated grains in southern China. The leaves of the three crops are very fragile and sensitive and are susceptible to leaf diseases, such as leaf blight of corn, late blight of potato and mosaic virus of tomato. These diseases cannot be identified at early stages. Therefore, an efficient solution is proposed by deep learning techniques to detect the disease categories of crops, which can effectively prevent the spread of diseases and ensure the normal growth of plants. In this experiment, our model achieved an overall accuracy of 99.55% for the identification of the three diseases of corn, potato and tomato. In addition, we tested the three plants individually. The classification accuracy of our model on corn, potato and tomato was 98.44%, 99.43% and 95.20%, respectively. We have also developed a web-based real-time plant disease classification system and deployed our model. The system had good performance in time and accuracy evaluation metrics. The results of this study showed that our model had fewer parameters, shorter training time, and higher recognition accuracy compared to existing image classification models.
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