Towards Infield Navigation: leveraging simulated data for crop row detection

De Silva, Rajitha, Cielniak, Grzegorz and Gao, Junfeng (2022) Towards Infield Navigation: leveraging simulated data for crop row detection. In: IEEE International Conference on Automation Science and Engineering (CASE).

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Towards Infield Navigation: leveraging simulated data for crop row detection
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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

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 alongwith 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 realworld data. Our model performed well against field variations such as shadows, sunlight and growth 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.

Keywords:Deep learning, crop row detection, Synthetic images
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
H Engineering > H671 Robotics
D Veterinary Sciences, Agriculture and related subjects > D400 Agriculture
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:49913
Deposited On:30 Jun 2022 10:42

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