Abdelhedi, S., K. Taouil, and B. Hadjkacem. 2012. Design of Automatic Vision-based Inspection System for Monitoring in an Olive Oil Bottling Line. International Journal of Computer Applications 51: 39-46.
2. AhmadKhani, S., A. Mahmoodi, K. Mollahzade, and H. Ghafari. 2014. Prediction of peach fruit firmness using imaging system of laser light scattering. Biosystem Engineering 46: 229-234. (In Farsi).
3. Al Ohali, Y. 2011. Computer vision-based date fruit grading system: Design and implementation. Journal of King Saud University - Computer and Information Sciences 23: 29-36.
4. Arjenaki, O. O., P. A. Moghaddam, and A. M. Motlagh. 2013. Online tomato sorting based on shape, maturity, size, and surface defects using machine vision. Turkish Journal of Agriculture and Forestry 37: 62-68.
5. Avendano, J., P. J. Ramos, and F. A. Prieto. 2017. A system for classifying vegetative structures on coffee branches based on videos recorded in the field by a mobile device. Expert Systems with Applications 88: 178-192.
6. Azizi, A., Y. Abbaspour-Gilandeh, M. Nooshyar, and A. Afkari-Sayah. 2016. Identifying Potato Varieties Using Machine Vision and Artificial Neural Networks. International Journal of Food Properties 19: 618-635.
7. Bennedsen, B. S., and D. L. Peterson. 2005. Performance of a System for Apple Surface Defect Identification in Near-infrared Images. Biosystems Engineering 90: 419-431.
8. Blasco, J., S. Cubero, J. Gomez-Sanchis, P. Mira, and E. Molto. 2009. Development of a machine for the automatic sorting of pomegranate (Punica granatum) arils based on computer vision. Journal of Food Engineering 90: 27-34.
9. Duan, F., Y.-N. Wang, H.-J. Liu, and Y.-G. Li. 2007. A machine vision inspector for beer bottle. Engineering Applications of Artificial Intelligence 20: 1013-1021.
10. ElMasry, G., S. Cubero, E. Molto, and J. Blasco. 2012. In-line sorting of irregular potatoes by using automated computer-based machine vision system. Journal of Food Engineering 112: 60-68.
11. Fehr, B. W., and J. B. Gerrish. 1995. Vision-guided row crop follower. Applied Engineering in Agriculture 11: 613-620.
12. Guyer, D., and X. Yang. 2000. Use of genetic artificial neural networks and spectral imaging for defect detection on cherries. Computers and Electronics in Agriculture 29: 179-194.
13. Islam, M., R. Sahriar, and B. Hossain. 2012. An enhanced automatic surface and structural flaw inspection and categorization using image processing both for flat and textured ceramic tiles. International Journal of Computer Applications 48: 1-10.
14. Jin, J., J. Li, G. Liao, X. Yu, L. Christopher, and C. Viray. 2009. Methodology for Potatoes Defects Detection with Computer Vision. in International Symposium on Information Processing. Huangshan, P. R. China.
15. Jinshi, C., Y. Myongkyoon, S. Daesik, and C. Seong-In. 2017. Machine vision and thermographic imaging for determining of grading of tomato on postharvest. 2017 ASABE Annual International Meeting: 1.
16. Khan, S., T. Mulani, P. Lalge, and N. Shaikh. 2017. An Image Processing Technique for Grading of Harvested Mangoes. International Research Journal of Engineering and Technology 4: 825-828.
17. Kheiralipour, K., and A. Pormah. 2017. Introducing new shape features for classification of cucumber fruit based on image processing technique and artificial neural networks. Journal of Food Process Engineering: DOI: 10.1111/jfpe.12558.
18. KheyrAlipour, K., V. Mohammadi, and M. GhasemiVarnamkhasti. 2014. Persimmon fruit ripening detection using image processing and support vector machine. 9th National Congress of Agricultural Machinery Engineering (Mechanical Biosystems and mechanization). Tehran University. (In Farsi).
19. Kumar, D. P., and K. Kannan. 2010. A roadmap for designing an automated visual inspection system. International Journal of Computer Applications 1: 34-37.
20. Manickavasagan, A., N. K. Al-Mezeini, and H. N. Al-Shekaili. 2014. RGB color imaging technique for grading of dates. Scientia Horticulturae 175: 87-94.
21. Mozina, M., D. Tomaževič, F. Pernuš, and B. Likar. 2009. Real-time image segmentation for visual inspection of pharmaceutical tablets. Machine Vision and Applications 22: 145-156.
22. Nouri-Ahmadabadi, H., M. Omid, S. S. Mohtasebi, and M. Soltani Firouz. 2017. Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine. Information processing in agriculture: http://dx.doi.org/10.1016/j.inpa.2017.1006.1002.
23. Omidi, A. A., P. ModaresMotlagh, and M. Ahmadi. 2012. Design, construction and evaluation of intelligent grading system of tomato. 9th National Congress of Agricultural Machinery Engineering (Mechanical Biosystems and mechanization). Shiraz University. (In Farsi).
24. Tachwali, Y., Y. Al-Assaf, and A. R. Al-Ali. 2007. Automatic multistage classification system for plastic bottles recycling. Resources, Conservation and Recycling 52 (2): 266-285.
25. Teymori, N., M. Omid, K. MoharamZadeh, and A. Rajabipour. 2015. Almond separation of conjoined and qualitative classification of a combination of image processing techniques and networks ANN. Biosystem Engineering 46: 355-362. (In Farsi).
26. Younes, M. A., S. Darwish, and M. El-Sayed. 2011. Online quality monitoring of perforated steel strips using an automated visual inspection (AVI) system. in IEEE International Conference on Quality and Reliability (ICQR). Bangkok, Thailand
27. Yousefdad, M., A. M. AfkariSayah, M. R. Larijani, and Y. AbbaspourGilande. 2014. Compare kiwi healthy and defective fruits based on color components using image processing techniques. in Advances in engineering and basic science. Tehran. (In Farsi).
28. Zhang, Y., X. Yin, X. Zou, and J. Zhao. 2009. On-line sorting maturity of cherry tomato by machine vision. International Federation for Information Processing 295: 2223-2230.