End-to-end Learning for Automomous Crop Row-following

Bakken, Marianne, Moore, Richard James Donald and From, Pal (2019) End-to-end Learning for Automomous Crop Row-following. In: International Federation of Automatic Control (IFAC), 4-6 December 2019, Sydney, Australia.

Full content URL: https://doi.org/10.1016/j.ifacol.2019.12.505

End-to-end Learning for Automomous Crop Row-following
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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


For robotic technology to be adopted within the agricultural domain, there is a need for low-cost systems that can be flexibly deployed across a wide variety of crop types, environmental conditions, and planting methods, without extensive re-engineering. Here we present an approach for predicting steering angles for an autonomous, crop row-following, agri-robot using only RGB image input. Our approach employs a deep convolutional neural network (DCNN) and an end-to-end learning strategy. We pre-train our network using existing open datasets containing natural features and show that this approach can help to preserve performance across diverse agricultural settings. We also present preliminary results from open-loop field tests that demonstrate the feasibility and some of the limitations of this approach for agri-robot guidance.

Keywords:Automation and Robotics in Agriculture
Subjects:H Engineering > H671 Robotics
D Veterinary Sciences, Agriculture and related subjects > D470 Agricultural Technology
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:39241
Deposited On:06 Jan 2020 10:12

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