Multi-class SVM Based Classification Approach for Tomato Ripeness

Author:

Elhariri Esraa,El-Bendary Nashwa,Fouad Mohamed Mostafa M.,Platoš Jan,Hassanien Aboul Ella,Hussein Ahmed M. M.

Publisher

Springer International Publishing

Reference23 articles.

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2. May, Z., Amaran, M.H.: Automated ripeness assessment of oil palm fruit using RGB and fuzzy logic technique. In: Demiralp, M., Bojkovic, Z., Repanovici, A. (eds.) Proc. the 13th WSEAS International Conference on Mathematical and Computational Methods in Science and Engineering (MACMESE 2011), Wisconsin, USA, pp. 52–59 (2011)

3. Polder, G., van der Heijden, G.W.A.M., Young, I.T.: Spectral Image Analysis for Measuring Ripeness of Tomatoes. Transactions-American Society of Agricultural Engineers International Journal 45(4), 1155–1162 (2002)

4. Jaffar, A., Jaafar, R., Jamil, N., Low, C.Y., Abdullah, B.: Photogrammetric Grading of Oil Palm Fresh Fruit Bunches. International Journal of Mechanical & Mechatronics Engineering (IJMME) 9(10), 18–24 (2009)

5. Paulraj, M.P., Hema, C.R., Krishnan, R.P., Radzi, S.S.M.: Color Recognition Algorithm using a Neural Network Model in Determining the Ripeness of a Banana. In: Proc. the International Conference on Man-Machine Systems (ICoMMS), Penang, Malaysia, pp. 2B7-1–2B7-4 (2009)

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