RT Journal Article
T1 A new expert system for greenness identification in agricultural images
A1 Cruz García, Jesús Manuel de la
A1 Romeo Granados, Juan
A1 Pajares Martínsanz, Gonzalo
A1 Montalvo Martínez, Martin
A1 Guerrero Hernández, José Miguel
A1 Guijarro Mata-García, María
AB It is well-known that one important issue emerging strongly in agriculture is related with the automation of tasks, where camera-based sensors play an important role. They provide images that must be conveniently processed. The most relevant image processing procedures require the identification of green plants, in our experiments they comes from barley and maize fields including weeds, so that some type of action can be carried out, including site-specific treatments with chemical products or mechanical manipulations. The images come from outdoor environments, which are affected for a high variability of illumination conditions because of sunny or cloudy days or both with high rate of changes. Several indices have been proposed in the literature for greenness identification, but under adverse environmental conditions most of them fail or do not work properly. This is true even for camera devices with auto-image white balance. This paper proposes a new automatic and robust Expert System for greenness identification. It consists of two main modules: (1) decision making, based on image histogram analysis and (2) greenness identification, where two different strategies are proposed, the first based on classical greenness identification methods and the second inspired on the Fuzzy Clustering approach. The Expert System design as a whole makes a contribution, but the Fuzzy Clustering strategy makes the main finding of this paper. The system is tested for different images captured with several camera devices. (C) 2012 Elsevier Ltd. All rights reserved.
PB Pergamon-Elsevier Science LTD
SN 0957-4174
YR 2013
FD 2013-05
LK https://hdl.handle.net/20.500.14352/33327
UL https://hdl.handle.net/20.500.14352/33327
LA eng
NO © 2012 Elsevier Ltd. The research leading to these results has been funded by the European Union's Seventh Framework Programme [FP7/2007-2013] under Grant Agreement No. 245986 in the Theme NMP-2009-3.4-1 (Automation and robotics for sustainable crop and forestry management). The authors wish also to acknowledge to the project AGL2011-30442-C02-02, supported by the Ministerio de Economia y Competitividad of Spain within the Plan Nacional de I+D+i.
NO Unión Europea. FP7
NO Ministerio de Economía y Competitividad of Spain within the Plan Nacional de I+D+i
DS Docta Complutense
RD 9 abr 2025