Dataset and codes of "A parallel resnet-transformer and cross-modal attention network for identification of rice blast resistance using UAV-based visible-infrared imagery"

This work exploited the potential of UAV-based visible-infrared imagery for rice blast resistance (RBR) identification, which is of significance to promote the rice breeding and ensure global food security.<br>This repository contains three sub folders: 1. data: the dataset of this paper2. codes: the codes of this paper3. weights: the training weights of our model<br><br>------## Requirements<br>- Python 3.8- PyTorch 2.1.0- TorchVision- Anaconda environment recommended here!- GPU environment is required for training and testing<br><br>## Usage------## TrainingThe script of train.py is used for training.<br>## TestingThe script of test.py is used for testing.