Towards Infield Navigation: leveraging simulated data for crop row detection
Published in IEEE International Conference on Automation Science and Engineering (CASE), 2022
This paper studies how simulated agricultural imagery can be combined with smaller real-world datasets to train crop-row detection models more efficiently for field robotics.
Recommended citation: de Silva, R., Cielniak, G., & Gao, J. (2022). "Towards Infield Navigation: leveraging simulated data for crop row detection." IEEE International Conference on Automation Science and Engineering (CASE).
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