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dc.contributor.authorLameski, Petreen_US
dc.contributor.authorZdravevski, Eftimen_US
dc.contributor.authorTrajkovikj, Vladimiren_US
dc.contributor.authorKulakov, Andreaen_US
dc.date.accessioned2022-07-14T11:11:57Z-
dc.date.available2022-07-14T11:11:57Z-
dc.date.issued2017-09-18-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/20773-
dc.description.abstractWeed detection from images has received a great interest from scientific communities in recent years. However, there are only a few available datasets that can be used for weed detection from unmanned and other ground vehicles and systems. In this paper we present a new dataset (i.e. Carrot-Weed) for weed detection taken under variable light conditions. The dataset contains RGB images from young carrot seedlings taken during the period of February in the area around Negotino, Republic of Macedonia. We performed initial analysis of the dataset and report the initial results, obtained using convolutional neural network architectures.en_US
dc.publisherSpringer, Chamen_US
dc.subjectdataset, weed detection, machine learning, signal processing, precision agricultureen_US
dc.titleWeed detection dataset with RGB images taken under variable light conditionsen_US
dc.typeProceeding articleen_US
dc.relation.conferenceInternational Conference on ICT Innovationsen_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
crisitem.author.deptFaculty of Computer Science and Engineering-
crisitem.author.deptFaculty of Computer Science and Engineering-
crisitem.author.deptFaculty of Computer Science and Engineering-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers
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