  Agricultural datasets for crop row detection are often bound by their limited
number of images. This restricts the researchers from developing deep learning
based models for precision agricultural tasks involving crop row detection. We
suggest the utilization of small real-world datasets along with additional data
generated by simulations to yield similar crop row detection performance as
that of a model trained with a large real world dataset. Our method could reach
the performance of a deep learning based crop row detection model trained with
real-world data by using 60% less labelled real-world data. Our model performed
well against field variations such as shadows, sunlight and grow stages. We
introduce an automated pipeline to generate labelled images for crop row
detection in simulation domain. An extensive comparison is done to analyze the
contribution of simulated data towards reaching robust crop row detection in
various real-world field scenarios.
