Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields

Ponnambalam, Vignesh Raja, Bakken, Marianne, Moore, Richard J. D. , Glenn Omholt Gjevestad, Jon and From, Pal (2020) Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields. Sensors, 20 (18). p. 5249. ISSN 1424-8220

Full content URL:

Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields
Published Open Access manuscript
sensors-20-05249.pdf - Whole Document
Available under License Creative Commons Attribution 4.0 International.

Item Type:Article
Item Status:Live Archive


Automated robotic platforms are an important part of precision agriculture solutions for sustainable food production. Agri-robots require robust and accurate guidance systems in order to navigate between crops and to and from their base station. Onboard sensors such as machine vision cameras offer a flexible guidance alternative to more expensive solutions for structured environments such as scanning lidar or RTK-GNSS. The main challenges for visual crop row guidance are the dramatic differences in appearance of crops between farms and throughout the season and the variations in crop spacing and contours of the crop rows. Here we present a visual guidance pipeline for an agri-robot operating in strawberry fields in Norway that is based on semantic segmentation with a convolution neural network (CNN) to segment input RGB images into crop and not-crop (i.e., drivable terrain) regions. To handle the uneven contours of crop rows in Norway’s hilly agricultural regions, we develop a new adaptive multi-ROI method for fitting trajectories to the drivable regions. We test our approach in open-loop trials with a real agri-robot operating in the field and show that our approach compares favourably to other traditional guidance approaches.

Keywords:navigation and guidance, image processing, deep learning, automation and robotics
Subjects: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:44051
Deposited On:17 Feb 2021 10:44

Repository Staff Only: item control page