  Deep learning models have been used for a variety of image processing tasks.
However, most of these models are developed through supervised learning
approaches, which rely heavily on the availability of large-scale annotated
datasets. Developing such datasets is tedious and expensive. In the absence of
an annotated dataset, synthetic data can be used for model development;
however, due to the substantial differences between simulated and real data, a
phenomenon referred to as domain gap, the resulting models often underperform
when applied to real data. In this research, we aim to address this challenge
by first computationally simulating a large-scale annotated dataset and then
using a generative adversarial network (GAN) to fill the gap between simulated
and real images. This approach results in a synthetic dataset that can be
effectively utilized to train a deep-learning model. Using this approach, we
developed a realistic annotated synthetic dataset for wheat head segmentation.
This dataset was then used to develop a deep-learning model for semantic
segmentation. The resulting model achieved a Dice score of 83.4\% on an
internal dataset and Dice scores of 79.6% and 83.6% on two external Global
Wheat Head Detection datasets. While we proposed this approach in the context
of wheat head segmentation, it can be generalized to other crop types or, more
broadly, to images with dense, repeated patterns such as those found in
cellular imagery.
