Journal
ICT INNOVATIONS 2017: DATA-DRIVEN INNOVATION
Volume 778, Issue -, Pages 112-119Publisher
SPRINGER-VERLAG BERLIN
DOI: 10.1007/978-3-319-67597-8_11
Keywords
Dataset; Weed detection; Machine learning; Signal processing; Precision agriculture
Funding
- Cyril and Methodius in Skopje, Macedonia, Faculty of Computer Science and Engineering
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Weed 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.
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