Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-67597-8_11 | Communications in Computer and Information Science |
Keywords | DocType | Volume |
Dataset,Weed detection,Machine learning,Signal processing,Precision agriculture | Conference | 778 |
ISSN | Citations | PageRank |
1865-0929 | 2 | 0.42 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Petre Lameski | 1 | 61 | 13.84 |
| Eftim Zdravevski | 2 | 57 | 16.51 |
| Vladimir Trajkovik | 3 | 47 | 17.70 |
| Andrea Kulakov | 4 | 98 | 14.79 |