  In agricultural image analysis, optimal model performance is keenly pursued
for better fulfilling visual recognition tasks (e.g., image classification,
segmentation, object detection and localization), in the presence of challenges
with biological variability and unstructured environments. Large-scale,
balanced and ground-truthed image datasets, however, are often difficult to
obtain to fuel the development of advanced, high-performance models. As
artificial intelligence through deep learning is impacting analysis and
modeling of agricultural images, data augmentation plays a crucial role in
boosting model performance while reducing manual efforts for data preparation,
by algorithmically expanding training datasets. Beyond traditional data
augmentation techniques, generative adversarial network (GAN) invented in 2014
in the computer vision community, provides a suite of novel approaches that can
learn good data representations and generate highly realistic samples. Since
2017, there has been a growth of research into GANs for image augmentation or
synthesis in agriculture for improved model performance. This paper presents an
overview of the evolution of GAN architectures followed by a systematic review
of their application to agriculture
(https://github.com/Derekabc/GANs-Agriculture), involving various vision tasks
for plant health, weeds, fruits, aquaculture, animal farming, plant phenotyping
as well as postharvest detection of fruit defects. Challenges and opportunities
of GANs are discussed for future research.
