A hybrid vision transformer and ResNet18 based model for biotic rice leaf disease detection.

Abstract

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.

Plant phenotyping relevance

イネ葉の病害状態を画像から分類する深層学習手法の提案・比較評価が研究の中心であり、植物病害フェノタイピング手法に該当する。

Code and data availability

The paper trains a hybrid ViT-ResNet18 model for rice leaf disease classification on public rice leaf disease image datasets. Two public image datasets are cited in the references with explicit URLs: the Kaggle rice diseases image dataset and the Mendeley rice leaf diseases dataset. No authors' analysis code or trained