In the traditional deep learning models for crop disease identification,there are problem of low detection accuracy and efficiency.A lightweight and improved MobileNet V2 model,namely CA-MobileNet V2(coordinate attention),was proposed for the above problem,which was easy to use by growers while improving the detection accuracy and deploying on mobile termi-nal.The lightweight coordination attention module was embedded in MobileNet V2 to improve accuracy with almost no computa-tional overhead.TanhExp activation function was added for the lightweight network to accelerate model convergence and enhance model robustness and generalization.The model was deployed to the mobile APP,so that the model had better visual application effects.The results of comparison experiments on PantifyDr and Turkey-PlantDataset datasets show that CA-MobileNet V2 has the advantages of high accuracy and light weight.
维普
万方数据