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A new expert system for greenness identification in agricultural images

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Cruz García, Jesús Manuel de la and Romeo Granados, Juan and Pajares Martinsanz, Gonzalo and Montalvo Martínez, Martin and Guerrero Hernández, José Miguel and Guijarro Mata-García, Marta (2013) A new expert system for greenness identification in agricultural images. Expert Systems with Applications, 40 (6). pp. 2275-2286. ISSN 0957-4174

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Official URL: http://dx.doi.org/10.1016/j.eswa.2012.10.033


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Abstract

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.


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© 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.

Uncontrolled Keywords:Environmentally Adaptive Segmentation, Threshold Selection, Digital Images, Maize Fields, Color, Vegetation, Algorithm, Pressure, Features, Plant.
Subjects:Sciences > Computer science
ID Code:22193
Deposited On:01 Aug 2013 08:01
Last Modified:04 Mar 2015 09:35

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