Perez-Ortiz, M;
Manuel Pena, J;
Antonio Gutierrez, P;
Torres-Sanchez, J;
Hervas-Martinez, C;
Lopez-Granados, F;
(2016)
Selecting patterns and features for between- and within-crop-row weed mapping using UAV-imagery.
Expert Systems with Applications
, 47
pp. 85-94.
10.1016/j.eswa.2015.10.043.
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Abstract
This paper approaches the problem of weed mapping for precision agriculture, using imagery provided by Unmanned Aerial Vehicles (UAVs) from sunflower and maize crops. Precision agriculture referred to weed control is mainly based on the design of early post-emergence site-specific control treatments according to weed coverage, where one of the most important challenges is the spectral similarity of crop and weed pixels in early growth stages. Our work tackles this problem in the context of object-based image analysis (OBIA) by means of supervised machine learning methods combined with pattern and feature selection techniques, devising a strategy for alleviating the user intervention in the system while not compromising the accuracy. This work firstly proposes a method for choosing a set of training patterns via clustering techniques so as to consider a representative set of the whole field data spectrum for the classification method. Furthermore, a feature selection method is used to obtain the best discriminating features from a set of several statistics and measures of different nature. Results from this research show that the proposed method for pattern selection is suitable and leads to the construction of robust sets of data. The exploitation of different statistical, spatial and texture metrics represents a new avenue with huge potential for between and within crop-row weed mapping via UAV-imagery and shows good synergy when complemented with OBIA. Finally, there are some measures (specially those linked to vegetation indexes) that are of great influence for weed mapping in both sunflower and maize crops.
Type: | Article |
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Title: | Selecting patterns and features for between- and within-crop-row weed mapping using UAV-imagery |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1016/j.eswa.2015.10.043 |
Publisher version: | https://doi.org/10.1016/j.eswa.2015.10.043 |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Remote sensing, Unmanned aerial vehicles (UAV), Weed detection, Object based image analysis |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10062164 |
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