A novel twin vision transformer framework for crop disease classification with deformable attention

In recent years, crop disease has grown substantially due to the lack of immunity in crops and climate changes. This results in crop destruction and poor crop cultivation. Quick and accurate classification of crop disease is crucial for enhancing agricultural yield and preventing crop damage. This research proposes a novel Twin Vision Transformer framework for accurate and quick classification of crop disease. The proposed model utilized a Depthwise Separable Visual Geometry Group (16) technique to extract the crop image features that incorporated Depthwise Separable Convolution and channel normalization. The Depthwise Separable Convolution is deployed for computing the channel and space separation and mitigating the computational complexity. The channel normalization is employed to normalize each activation using local responses. Further, the Twin Vision Transformer model integrates a transformer encoder, Edge Aware Enhancement Module, and classifier head for accurate disease classification. The transformer encoder layer is applied to capture the complex relationship that encompasses deformable attention for identifying the image patch’s relevant part and enhancing the flexibility of the model, and spatial-channel attention for enhancing the crucial feature channels and spatial locations. Moreover, the residual Multilayer Perceptron with Swish-Gated Linear Units is employed to enhance the generalization capability of the model by mitigating overfitting. The Edge Aware Enhancement Module is developed to improve the features around the identified edges. Additionally, the Squeeze and Excitation block in the classifier head is applied to emphasize the features of crop disease and to enhance the model’s classification accuracy. The proposed Twin Vision Transformer model provided a higher classification accuracy of 98.88 %, and the experimental analysis confirms that the proposed Twin Vision Transformer model is more accurate and reliable in classifying crop disease than existing crop disease classification approaches.