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Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 3478602, 17 pages
Research Article

Robust Grape Detector Based on SVMs and HOG Features

Department of Process Control, University of Pardubice, Pardubice, Czech Republic

Correspondence should be addressed to Pavel Škrabánek; zc.ecpu@kenabarks.levap

Received 2 February 2017; Accepted 23 April 2017; Published 18 May 2017

Academic Editor: Michael Schmuker

Copyright © 2017 Pavel Škrabánek and Petr Doležel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Detection of grapes in real-life images is a serious task solved by researchers dealing with precision viticulture. In the case of white wine varieties, grape detectors based on SVMs classifiers, in combination with a HOG descriptor, have proven to be very efficient. Simplified versions of the detectors seem to be the best solution for practical applications. They offer the best known performance versus time-complexity ratio. As our research showed, a conversion of RGB images to grayscale format, which is implemented at an image preprocessing level, is ideal means for further improvement of performance of the detectors. In order to enhance the ratio, we explored relevance of the conversion in a context of a detector potential sensitivity to a rotation of berries. For this purpose, we proposed a modification of the conversion, and we designed an appropriate method for a tuning of such modified detectors. To evaluate the effect of the new parameter space on their performance, we developed a specialized visualization method. In order to provide accurate results, we formed new datasets for both tuning and evaluation of the detectors. Our effort resulted in a robust grape detector which is less sensitive to image distortion.