GoogLeNet based on residual network and attention mechanism identification of rice leaf diseases

Abstract

Rice leaf diseases are a major cause of declining rice production and quality. The early identification and control of rice leaf diseases is critical for maintaining rice quality and production. However, early rice leaf diseases appear in natural scene images as small spots with irregular shapes, and existing models have difficulty accurately identifying rice leaf diseases. This study proposes a rE-GoogLeNet convolutional neural network model for accurately identifying rice leaf diseases in natural environments. rE-GoogLeNet is based on GoogLeNet and replaces the 7 × 7 convolution kernel in the first layer with three 3 × 3 convolution kernels and adds an ECA attention mechanism to the Inception module. This new module is named the E-Inception module, and a residual network is used to connect the E-Inception modules, addressing the issues of increased information loss and gradient loss due to increasing network depth. Finally, the ReLU activation function is replaced by the leaky ReLU activation function, improving the feature extraction ability for diseased leaves with irregularly shaped small spots. Moreover, the auxiliary classifier is simplified, reducing the complexity of the model without decreasing the recognition rate. Experiments show that compared with conventional models and advanced multiscale models, rE-GoogLeNet has a better classification performance on rice leaf diseases and an average accuracy of 99.58 %, an improvement of 1.72 % over the original GoogLeNet model. This evaluation shows that rE-GoogLeNet is robust, stable and highly accurate for detecting rice leaf diseases in practical applications. The evaluation also shows that rE-GoogLeNet has a good recall rate, F1 score and confusion matrix.