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Agricultural datasets for crop row detection are often bound by their limitednumber of images. This restricts the researchers from developing deep learningbased models for precision agricultural tasks involving crop row detection. Wesuggest the utilization of small real-world datasets along with additional datagenerated by simulations to yield similar crop row detection performance asthat of a model trained with a large real world dataset. Our method could reachthe performance of a deep learning based crop row detection model trained withreal-world data by using 60% less labelled real-world data. Our model performedwell against field variations such as shadows, sunlight and grow stages. Weintroduce an automated pipeline to generate labelled images for crop rowdetection in simulation domain. An extensive comparison is done to analyze thecontribution of simulated data towards reaching robust crop row detection invarious real-world field scenarios.

Towards Infield Navigation: leveraging simulated data for crop row detection