Title Selecting patterns and features for between- and within-crop-row weed mapping using UAV-imagery
Authors PÉREZ ORTIZ, MARÍA, Manuel Pena, Jose, Antonio Gutierrez, Pedro, Torres-Sanchez, Jorge, Hervas-Martinez, Cesar, Lopez-Granados, Francisca, PÉREZ ORTIZ, MARÍA
External publication No
Means Expert Syst. Appl.
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 1
JCR Impact 3.92800
SJR Impact 1.34300
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949520283&doi=10.1016%2fj.eswa.2015.10.043&partnerID=40&md5=77d3329c6c9ad70c539c3f0a1fb38a3b
Publication date 01/04/2016
ISI 000368967900008
Scopus Id 2-s2.0-84949520283
DOI 10.1016/j.eswa.2015.10.043
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. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords Remote sensing; Unmanned aerial vehicles (UAV); Weed detection; Object based image analysis
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