Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review. (arXiv:2204.04707v2 [cs.CV] UPDATED)

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 …

agriculture arxiv augmentation cv generative adversarial networks image networks review