Simplified Version of White Wine Grape Berries Detector Based on SVM and HOG Features
The detection of grapes in real scene images is a serious task solved by researches dealing with precision viticulture. Our research has shown that in the case of white wine varieties, grape berry detectors based on a support vector machine classifier in combination with a HOG descriptor are very efficient. In this paper, simplified versions of our original solutions are introduced. Our research showed that skipping contrast normalization by image preprocessing accelerates the detection process; however, the performance of the detectors is not negatively influenced by this modification.
KeywordsComputer vision Precision viticulture Grape detection Support vector machine HOG features
The work has been supported by the Funds of University of Pardubice, Czech Republic. We would like to offer our special thanks to company Víno Sýkora s.r.o. which enabled us to perform experiments in its vineyards.
- 1.Arnó Satorra, J., Martínez Casasnovas, J.A., Ribes Dasi, M., Rosell Polo, J.R.: Review. Precision viticulture. Research topics, challenges and opportunities in site-specific vineyard management. Span. J. Agric. Res. 7(4), 779–790 (2009)Google Scholar
- 3.Chamelat, R., Rosso, E., Choksuriwong, A., Rosenberger, C., Laurent, H., Bro, P.: Grape detection by image processing. In: IECON 2006—32nd Annual Conference on IEEE Industrial Electronics, pp. 3697–3702 (2006)Google Scholar
- 4.Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 1, pp. 886–893 (2005)Google Scholar
- 6.Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Pearson, 2nd edn. (2012)Google Scholar
- 7.ITU-R Recommendation BT.601: Studio encoding parameters of digital television for standard 4:3 and wide screen 16:9 aspect ratios (2011)Google Scholar
- 8.Krig, S.: Computer Vision Metrics: Survey, Taxonomy, and Analysis, 1st edn. Apress, Berkely, CA, USA (2014)Google Scholar
- 10.Liu, S., Whitty, M.: Automatic grape bunch detection in vineyards with an SVM classifier. J. Appl. Logic (2015)Google Scholar
- 11.Nuske, S., Achar, S., Bates, T., Narasimhan, S., Singh, S.: Yield estimation in vineyards by visual grape detection. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2352–2358. IEEE (2011)Google Scholar
- 14.Škrabánek, P., Runarsson, T.P.: Detection of grapes in natural environment using support vector machine classifier. In: Proceedings of the 21st International Conference on Soft Computing MENDEL 2015, Brno University of Technology, Brno, Czech Republic, 23–25 Jun 2015, pp. 143–150 (2015)Google Scholar