2024-03-28T22:44:03Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/455162017-02-02T10:52:27Zcom_10261_129com_10261_6com_10261_96com_10261_4col_10261_382col_10261_349
A new vision-based approach to differential spraying in precision agriculture
Tellaeche, A.
Burgos Artizzu, Xavier P.
Pajares, Gonzalo
Ribeiro Seijas, Ángela
Fernández-Quintanilla, César
Precision agriculture
Machine vision
Weed detection
Image segmentation
Multicriteria decision-making
12 páginas, ilustraciones y tablas estadísticas.
One of the objectives of precision agriculture is to minimize the volume of herbicides by
using site-specific weed management systems. To reach this goal, two major factors need
to be considered: (1) the similarity of spectral signatures, shapes, and textures between
weeds and crops and (2) irregular distribution of weeds within the crop. This paper outlines
an automatic computer vision method for detecting Avena sterilis, a noxious weed growing
in cereal crops, and differential spraying to control the weed. The proposed method
determines the quantity and distribution of weeds in the crop fields and applies a decisionmaking
strategy for selective spraying, which forms the main focus of the paper. The method
consists of two stages: image segmentation and decision-making. The image segmentation
process extracts cells from the image as the low-level units. The quantity and distribution
of weeds in the cell are mapped as area and structural based attributes, respectively. From
these attributes, a multicriteria decision-making approach under a fuzzy context allows us
to decide whether any given cell needs to be sprayed. The method was compared with other
existing strategies.
2012-02-14T07:51:19Z
2012-02-14T07:51:19Z
2008
artículo
Cumputers and Electronics in Agriculture 60: 144-155 (2008)
0168-1699
http://hdl.handle.net/10261/45516
10.1016/j.compag.2007.07.008
eng
http://dx.doi.org/10.11016/j.compag.2007.07.008
closedAccess
Elsevier