TrIncNet: a lightweight vision transformer network for identification of plant diseases.In the agricultural sector, identifying plant diseases at their earliest possible stage of infestation still remains a huge challenge with respect to the maximization of crop production and farmers' income.  Therefore, this study proposes a comparatively lightweight and improved vision transformer network, also known as "TrIncNet" for plant disease identification.  The comparative performance analysis on both datasets reported that the proposed TrIncNet network outperformed the state-of-the-art CNN architectures viz: VGG-19, GoogLeNet, ResNet-50, Xception, InceptionV3, and MobileNet. 
TrIncNet
In recent years, advanced computer vision techniques like Vision Transformers (ViTs) are being successfully applied to identify plant diseases automatically. 
Vision Transformers (ViTs)
However, the MLP module in existing ViTs is computationally expensive as well as inefficient in extracting promising features from diseased images. 
MLP module
In the proposed network, we introduced a modified encoder architecture a.k.a. Trans-Inception block in which the MLP block of existing ViT was replaced by a custom inception block.  Additionally, each Trans-Inception block is surrounded by a skip connection, making it much more resistant to the vanishing gradient problem. 
Trans-Inception block
Additionally, each Trans-Inception block is surrounded by a skip connection, making it much more resistant to the vanishing gradient problem. 
skip connection