Classification model of citrus disease leaf based on improved Swin-Transformer
To address the problems of low efficiency,high cost,and low accuracy in manual detection of citrus diseases,this article combines artificial intelligence technology to classify and identify diseased citrus leaves.Firstly,a dataset of citrus disease leaves under simulated complex environments is established.Secondly,an improved Swin Transformer model for citrus disease leaf classification is proposed,which includes a Local Perception Channel Enhanced Attention Module(LPCE)to enhance the model's receptive field and feature representation capabilities.Through weighted correlation between channels,the model is made to extract key features more easily.Experiments demonstrate that the classification accuracy of the proposed model reaches 98.52%,with Precision,Recall,and F1-score reaching 98.17%,98.24%,and 98.28%respectively,all exceeding the baseline model.It providing technical support for the detection of citrus diseases.
万方数据
维普
