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End-to-end learning for autonomous crop row-following

Abstract

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.
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Category

Academic article

Language

English

Author(s)

Affiliation

  • SINTEF Digital / Smart Sensors and Microsystems
  • Norwegian University of Life Sciences

Year

2019

Published in

IFAC-PapersOnLine

Volume

52

Issue

30

Page(s)

102 - 107

View this publication at Norwegian Research Information Repository