Sensors 2015, 15, 5609-5626; doi:10.3390/s150305609
sensors
ISSN 1424-8220
www.mdpi.com/journal/sensors
Article
Quantifying Efficacy and Limits of Unmanned Aerial Vehicle
(UAV) Technology for Weed Seedling Detection as Affected by
Sensor Resolution
José M. Peña *, Jorge Torres-Sánchez, Angélica Serrano-Pérez, Ana I. de Castro and
Francisca López-Granados
Institute for Sustainable Agriculture, IAS-CSIC, P.O. Box 4084, 14080 Córdoba, Spain;
E-Mails: jtorres@ias.csic.es (J.T.-S.); aserrano@ias.csic.es (A.S.-P.); anadecastro@ias.csic.es (A.I.C.);
flgranados@ias.csic.es (F.L.-G.)
* Author to whom correspondence should be addressed; E-Mail: jmpena@ias.csic.es;
Tel.: +34-957-499-265.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 7 January 2015 / Accepted: 27 February 2015 / Published: 6 March 2015
Abstract: In order to optimize the application of herbicides in weed-crop systems,
accurate and timely weed maps of the crop-field are required. In this context, this
investigation quantified the efficacy and limitations of remote images collected with an
unmanned aerial vehicle (UAV) for early detection of weed seedlings. The ability to
discriminate weeds was significantly affected by the imagery spectral (type of camera),
spatial (flight altitude) and temporal (the date of the study) resolutions. The colour-infrared
images captured at 40 m and 50 days after sowing (date 2), when plants had 5–6 true
leaves, had the highest weed detection accuracy (up to 91%). At this flight altitude, the
images captured before date 2 had slightly better results than the images captured later.
However, this trend changed in the visible-light images captured at 60 m and higher, which
had notably better results on date 3 (57 days after sowing) because of the larger size of the
weed plants. Our results showed the requirements on spectral and spatial resolutions
needed to generate a suitable weed map early in the growing season, as well as the best
moment for the UAV image acquisition, with the ultimate objective of applying site-specific
weed management operations.
Keywords: remote sensing; visible-light and multispectral cameras; object-based image
analysis (OBIA); weed mapping; site-specific weed management (SSWM)
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