Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 39, Issue 12, Pages 11149-11155Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2012.03.040
Keywords
Support Vector Machines; Image segmentation; Weeds/crop discrimination; Precision Agriculture
Categories
Funding
- European Union [245986, NMP-2009-3.4-1]
- Ministerio de Economia y Competitividad of Spain [AGL2011-30442-C02-02]
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In Precision Agriculture (PA) automatic image segmentation for plant identification is an important issue to be addressed. Emerging technologies in optical imaging sensors play an important role in PA. In maize fields, site-specific treatments, with chemical products or mechanical manipulations, are applied for weeds elimination. Maize is an irrigated crop, also unprotected from rainfall. After a strong rain, soil materials (particularly clays) mixed with water impregnate the vegetative cover. The green spectral component associated to the plants is masked by the dominant red spectral component coming from soil materials. This makes methods based on the greenness identification fail under such situations. We propose a new method based on Support Vector Machines for identifying plants with green spectral components masked and unmasked. The method is also valid for post-treatment evaluation, where loss of greenness in weeds is identified with the effectiveness of the treatment and in crops with damage or masking. The performance of the method allows to verify its viability for automatic tasks in agriculture based on image processing. (C) 2012 Elsevier Ltd. All rights reserved.
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