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A hybrid vision transformer and ResNet18 based model for biotic rice leaf disease detection
Sankar Sennan1, Ramasubbareddy Somula2, Yongyun Cho1
1Department of Information and Communication Engineering, Sunchon National University, Suncheon, Republic of Korea.
Introduction:
Agriculture is crucial to human survival. The growing of biotic rice plants is very helpful for feeding a lot of people around the world, especially in places where rice is a main food. The detection of rice leaf disease is critical to increasing crop productivity.
Methods:
To improve the accuracy of rice leaf disease prediction, this paper proposes a hybrid Vision Transformer (ViT) with pre-trained ResNet18 models (ViT-ResNet18). In general, the input images apply to the pre-trained ViT and ResNet18 models independently. The output features of these two models are combined and fed into the final Fully Connected (FC) layer, followed by a Softmax layer for final classification.
Results:
The output of rice leaf diseases from the FC layer of the proposed hybrid ViT with ResNet18 model achieved 94.4% accuracy, a precision of 0.948, a recall of 0.944, an F1-Score of 0.942, and an Area Under Curve (AUC) of 0.985.
Discussion:
The proposed hybrid model ViT-ResNet18 shows a 5%, 1%, and 1% improvement in accuracy compared to VGG16 with Neural Network, Inception V3 with Neural Network, and SqueezeNet with Neural Network classifier, respectively.
