Semantic Consistent Tomato Leaf Disease Recognition Method Based on Data Augmentation
In response to the challenge of insufficient training images for tomato leaf disease rec-ognition,an innovative semantic-consistent tomato leaf disease recognition method is proposed based on data augmentation.Firstly,a data augmentation module is designed to effectively expand the dataset.Next,a deep feature extraction module is defined to capture rich nonlinear semantic infor-mation from the images.To mitigate semantic drift during the data augmentation process,a seman-tic relevance maximization module is introduced to enhance the semantic consistency between origi-nal and augmented data.Finally,a leaf disease recognition module is defined to achieve tomato leaf disease identification.Experimental results indicate that our approach outperforms eight baseline methods in terms of accuracy.
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