Anonymous. (1997). United States Standards for Grades of Fresh Tomatoes, Reprinted January 1997. USA.
Anonymous. (2016). FAOSTAT. Retrieved Oct. 12, 2016, from http://faostat.fao.org/site/567/DesktopDefault.aspx?PageID=567#ancor.
Beak, I. S., Cho, B. K. & Kim, Y. S. (2012). Development of a compact quality sorting machine for cherry tomatoes based on real-time color image processing. International Conference of Agricultural Engineering, 8-12 Sep. Valencia, Spain.
Davis, J. N. & Hobson, G. E. (1981). The constituents of tomato fruit the influence of environment, nutrition and genotype, CRC Critical Review in Food Science Nutrition, 15, 205-280.
Iraji M. S. & Tosinia, A. (2011). Classification of tomatoes by machine vision with fuzzy the Mamdani inference, adaptive neuro fuzzy inference system based (Anfis - Sugeno). Australian Journal of Basic and Applied Sciences. 5, 846-853.
Kavdir I. & Guyer, D. (2003). Apple Grading Using Fuzzy Logic. Department of Agriculture Machinery, 27, 375-382.
Lino, A.C.L. Sanches, J. & Fabbro, I. M. D. (2008). Image processing techniques for lemons and tomatoes classification. Journal of Bragantia, 67, 785- 789.
Lana, M. M., Tijskens L. M. M. & Van Kooten, O. (2005). Effects of storage temperature and fruit ripening on firmness of fresh cut tomatoes. Postharvest Biology and Technology, 35, 87-95.
Lana, M.M., Tijskens L. M. M. & Van Kooten, O. (2006). Effects of storage temperature and storage of ripening on RGB color aspects of fresh cut tomato pericarp using video image analysis. Journal of Food Engineering, 77, 871-879.
Miro, S., Hartmann, D. & Schanz, T. (2013). Global sensitivity analysis for subsoil parameter estimation in mechanized tunnelling. Computers and Geotechnics, 56, 80-88.
Nassiri, S.M., Khajavi, S. & Ramezaniyan, A. (2014). Image processing application to determine the color of tomato lycopene content in different temperature conditions. The first national conference on new technologies and post-harvest agricultural products, Agriculture and Natural Resources Research Center of Khorasan Razavi, 18-19 February. Mashhad. (In Farsi).
Nassiri, S.M., Tahavvor, A. & Jafari, A. (2016). Classification of mature tomato based on color, size and hardness using Fuzzy logic. 14th International Conference on Agricultural and Biosystems Engineering. Aarhus University, Aarhus, Denmark.
Omid, M. (2011). Design of an expert system for sorting pistachio nuts through decision tree and fuzzy logic classifier. Expert Systems with Applications, 38, 4339- 4347.
Plackett, R. L. 1983. Karl Pearson and the Chi-Squared Test, International Statistical Review, 51, 59–72.
Polder, G., Heijdena, G. W. A. M. & Young, I. T. (2003). Tomato sorting using independent component analysis on spectral images. Real-Time Imaging, 9, 253-259.
Sabery-Kamarposhty, R. & Pourreza, H. R. (2007). Classification and evaluation of image features of tomato by several image techniques. Third International Conference on Information and Knowledge Technology. 23 May, Ferdowsi University of Mashhad. Mashhad, Iran.
Schouten, R. E., Huijben, T. P. M., Tijskens, L. M. M. & Van Kooten, O. (2007). Modeling quality attributes of truss tomatoes: Linking color and firmness maturity. Postharvest Biology and Technology, 45, 298-306.
Tahavvor, A. (2014). Classification of mature tomato based on color, size and hardness using fuzzy logic. M.Sc. thesis on Mechanics of Agricultural Machinery, Shiraz University. Iran. (In Farsi).
Teshnehlab, M., Safarpour, N. & Afuni, D. (2010). Fuzzy systems and fuzzy control (1st ed). K. N. Toosi University of Technology Press. Pp 528.
Van Dijk, C., Boeriv, C., Peter, F., Stolle-smits, T. & Tijskens, L. M. M. (2006). The firmness of stored tomatoes (cv.Tradiro): kinetic and near infrared models to describe firmness and moisture loss. Journal of Food Engineering, 77, 575-584.
)
