  Large datasets' availability is catalyzing a rapid expansion of deep learning
in general and computer vision in particular. At the same time, in many
domains, a sufficient amount of training data is lacking, which may become an
obstacle to the practical application of computer vision techniques. This paper
challenges small and imbalanced datasets based on the example of a plant
phenomics domain. We introduce an image augmentation framework, which enables
us to extremely enlarge the number of training samples while providing the data
for such tasks as object detection, semantic segmentation, instance
segmentation, object counting, image denoising, and classification. We prove
that our augmentation method increases model performance when only a few
training samples are available. In our experiment, we use the DeepLabV3 model
on semantic segmentation tasks with Arabidopsis and Nicotiana tabacum image
dataset. The obtained result shows a 9% relative increase in model performance
compared to the basic image augmentation techniques.
