Abstract
Machine learning and computer vision techniques have applied for evaluating food quality as well as crops grading. In this paper, a new classification system has been proposed to classify infected/uninfected tomato fruits according to its external surface. The system is based on feature fusion method with color and texture features. Color moments, GLCM, and Wavelets energy and entropy have been used in the proposed system. Principle Component Analysis (PCA) technique has been used to reduce the feature vector obtained after fusion to avoid dimensionality problem and save time and cost. Support vector machine (SVM) was used to classify tomato images into 2 classes; infected/uninfected using Min-Max and Z-Score normalization methods. The dataset used in this research contains 177 tomato fruits each was captured from four faces (Top, Side1, Side2, and End). Using 70% of the total images for training phase and 30% for testing, our proposed system achieved accuracy 92%.
Keywords
- food quality
- feature fusion
- Color moments
- GLCM
- Wavelets
- Tomato
- PCA
- SVM
This is a preview of subscription content, access via your institution.
Buying options
Preview
Unable to display preview. Download preview PDF.
References
FAO Statistical Yearbook 2013- World food and agriculture. Rome, Italy: Food and Agriculture Organization of the United Nations (2011), http://faostat.fao.org/site/339/default.aspx
Du, C.J., Sun, D.W.: Learning techniques used in computer vision for food quality evaluation: a review. J. Food Engineering 72, 39–55 (2006)
Kodagali, J.A., Balaji, S.: Computer Vision and Image Analysis based Techniques for Automatic Characterization of Fruits - A Review. International Journal of Computer Applications 50(6), 6–12 (2012)
Gomes, J.F.S., Leta, F.R.: Applications of computer vision techniques in the agriculture and food industry: a review. Eur. Food Res. Technology 235(6), 989–1000 (2012)
Sankarana, S., Mishraa, A., Ehsania, R., Davisb, C.: A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture 72, 1–13 (2010)
Wang, H., Li, G., Ma, Z., Li, X.: Application of Neural Networks to Image Recognition of Plant Diseases. In: 2012 International Conference on Systems and Informatics (ICSAI 2012), pp. 2159–2164. IEEE (2012)
Arivazhagan, S., Newlin Shebiah, R., Selva Nidhyanandhan, S., Ganesan, L.: Fruit recognition using color and texture features. J. Emerging Trends in Computing and Information Sciences 1(2), 90–94 (2010)
Arjenaki, O.O., Moghaddam, P.A., Motlagh, A.M.: Online tomato sorting based on shape, maturity, size, and surface defects using machine vision. Turkish Journal of Agriculture and Forestry 37, 62–68 (2013)
Deepa, P., Geethalakshmi, S.N.: A Comparative Analysis of Feature Extraction Methods for Fruit Grading Classifications. International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS) 4(2), 221–225 (2013)
Ghaffari, R., Zhang, F., Iliescu, D., Hines, E., Leeson, M.S., Napier, R., Clarkson, J.: Early Detection of Diseases in Tomato Crops: An Electronic Nose and Intelligent Systems Approach. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–6. IEEE (2010)
Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics 3, 610–621 (1973)
Gadkari, D.: Image quality analysis using GLCM. University of Central Florida: Master of Science in Modeling and Simulation (2004)
Albregtsen, F.: Statistical texture measures computed from gray level coocurrence matrices. Image Processing Laboratory, Department of Informatics, University of Oslo, pp. 1–14 (1995)
Kocioek, M., Materka, A., Strzelecki, M., Szczypiki, P.: Discrete wavelet transform derived features for digital image texture analysis. In: International Conference on Signals and Electronic Systems, Lodz, Poland, September 18-21, pp. 163–168 (2001)
Tharwat, A., Ibrahim, A.F., Ali, H.A.: Multimodal biometric authentication algorithm using ear and finger knuckle images. In: Seventh IEEE International Conference on Computer Engineering and Systems (ICCES), pp. 176–179 (2012)
Jain, A., Nandakuma, K., Ross, A.: Score normalization in multimodal biometric systems. Pattern Recognition 38(12), 2270–2285 (2005)
Kuncheva, L.I.: Combining pattern classifiers: methods and algorithms, p. 18. John Wiley and Sons (2004)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognitive Neuroscience 3(1), 71–86 (1991)
Abe, S.: Support Vector Machines for Pattern Classification, Illustrated edn. Springer (2010)
Elhariri, E., El-Bendary, N., Fouad, M.M.M., Platos, J., Hassanien, A.E., Hussein, A.M.M.: Multi-class SVM Based Classification Approach for Tomato Ripeness. In: Abraham, A., Krömer, P., Snášel, V. (eds.) Innovations in Bio-inspired Computing and Applications. AISC, vol. 237, pp. 175–186. Springer, Heidelberg (2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Semary, N.A., Tharwat, A., Elhariri, E., Hassanien, A.E. (2015). Fruit-Based Tomato Grading System Using Features Fusion and Support Vector Machine. In: , et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-11310-4_35
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11309-8
Online ISBN: 978-3-319-11310-4
eBook Packages: EngineeringEngineering (R0)