In agricultural image analysis, optimal model performance is required for better fulfilling visual recognition tasks (e.g., classification, detection, and segmentation). Sufficient ground-truth datasets, however, are often difficult to obtain, posing a bottleneck to the development of advanced high-performance models. As artificial intelligence through deep learning is impacting analysis and modeling of agriculture images, data augmentation plays a vital role in boosting model performance by artificially generating new images to expand training datasets. Beyond traditional data augmentation techniques that rely on geometric and photometric transformations, generative adversarial network (GAN) invented in 2014 in the computer vision community, provides a suite of novel approaches that can learn good data representation and generate synthetic samples with the appearance of real images. Since 2018, there has been a growth of research into GANs for data augmentation or image synthesis for various agricultural applications. This repo contains GANs for augmenting agricultural images for improved model performance.
Please consider cite our paper if you find this repo is helpful.
@article{lu2022generative,
title={Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review},
author={Lu, Yuzhen and Chen, Dong and Olaniyi, Ebenezer and Huang, Yanbo},
journal={Computers and Electronics in Agriculture},
volume={200},
pages={107208},
year={2022},
publisher={Elsevier}
}
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