Plant leaf disease identification based on contrastive learning
At present,the recognition of plant disease via image processing mostly relies on the manually labeled convolutional neural network.However,the self-monitoring contrastive learning could achieve independent learning without relying on labels and large amounts of data.In view of this advantage,the effect of four contrastive learning methods,MoCo-v2,DeepCluster-v2,SwAV and BYOL,on the identification of plant leaf diseases were compared by setting different experimental conditions on the open-source dataset of PlantVillage and the self-built cotton disease dataset.The ResNet50 encoder trained by four contrastive learning methods was tested for disease identification both under Linear and Finetune modes,and the feasibility of the contrastive learning methods in identifying plant leaf dis-eases was evaluated.It was shown that the average accuracy under Finetune mode on the PlantVillage dataset was higher than that under Linear mode,and the highest identificaiton accuracy of the encoders trained by the four meth-ods reached 99.83%.DeepCluster-v2 and BYOL had the highest identification rate under Finetune mode,both of which were 99.87%.On the self-built cotton disease dataset,the performance under Finetune mode was poorer than that under Linear mode,and the highest idenficaiton accuracy of DeepCluster-v2 under Linear mode was 98.86%.Overall,the disease identification rate based on contrastive learning method was superior to the supervised models,demonstrating good application prospects in plant leaf disease identification.
万方数据
维普
