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Subject: dataset, weed detection, machine learning, signal processing, precision agriculture

Year: 2017

Type: Proceeding article

Title: Weed detection dataset with RGB images taken under variable light conditions

Author: Lameski, Petre
Author: Zdravevski, Eftim
Author: Trajkovikj, Vladimir
Author: Kulakov, Andrea

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.

Publisher: Springer, Cham

Relation: International Conference on ICT Innovations

Identifier: oai:repository.ukim.mk:20.500.12188/20773
Identifier: http://hdl.handle.net/20.500.12188/20773

Title	Date	Views
Weed detection dataset with RGB images taken under variable light conditions	2017	16