Integrating Global and Local Image Features for Plant Leaf Disease Recognition

To improve the accuracy of plant leaf disease image recognition, a CVT-based image classification algorithm is proposed. The algorithm utilizes Convolutional and Transformer networks for feature extraction and encoding, integrating global and local image features. By introducing the self-attention mechanism of Transformer, the algorithm achieves weather image data classification. Experimental results demonstrate that the CVT-based deep learning algorithm effectively enhances model prediction accuracy, showing promising results in plant leaf disease recognition. The algorithm achieves accurate recognition of five different classes of data, with an accuracy rate as high as 97.78%.