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dc.contributor.authorPonnambalam, Vignesh Raja
dc.contributor.authorBakken, Marianne
dc.contributor.authorMoore, Richard
dc.contributor.authorOmholt Gjevestad, Jon Glenn
dc.contributor.authorFrom, Pål Johan
dc.date.accessioned2021-05-26T11:44:00Z
dc.date.available2021-05-26T11:44:00Z
dc.date.created2020-11-11T11:35:59Z
dc.date.issued2020
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/11250/2756446
dc.description.abstractAutomated 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.
dc.language.isoeng
dc.relation.urihttps://www.mdpi.com/1424-8220/20/18/5249/htm
dc.titleAutonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionpublishedVersion
dc.source.volume20
dc.source.journalSensors
dc.source.issue18
dc.identifier.doi10.3390/s20185249
dc.identifier.cristin1846881
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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