Rice diseases have always been one of the important factors affecting rice yield.In order to quickly and accurately detect rice diseases,this study proposed a lightweight rice leaf disease recognition model based on convolutional neural network.Firstly,from the perspective of the number of parameters,the attention mechanism was improved to obtain a lightweight attention mechanism module,and the potential attention information in the rice leaf disease feature map was deeply mined.Secondly,the depthwise separable convolution was used to replace some standard convolutions to further re-duce the parameters of the model.Finally,in order to improve the generalization ability and make the model learning process faster and more stable,a method of combining the scaled exponential linear unit(SELU)activation function with internal normalization attribute and the external group normalization module was adopted.By verifying in the public data set,the average accuracy of the model constructed in this study was the highest(0.990 0).The model also had certain advantages in terms of parameter quantity and aver-age single iteration time.Compared with other models,it had relatively higher performance.
维普
万方数据