Rice Disease Recognition Based on Improved MobileNet Lightweight Network
Objective:To address the problem of the conventional MobileNet-v2 model in the recognition of rice leaf diseases,including its low recognition accuracy,sluggish running speed,and challenging feature extraction,a rice leaf disease recognition model based on improved MobileNet-v2 lightweight network was proposed.Methods:In this model,the method of adding attention mechanism module is used to enhance image feature extraction,and then the weight parameters of pre-trained model were trans-ferred to the improved model to identify the four leaf diseases of rice.Results:The training speed and overfitting issues were signifi-cantly reduced throughout the training and testing of the new-mobile model over 50 epochs,and the final test recognition accuracy was 7.3%higher than that of the conventional MobileNet-v2 model.Conclusions:The new mobile model is more accurate and quicker in recognizing rice leaf diseases,which provides a reference and significance for identifying and researching of rice leaf dis-eases.
万方数据
维普
