M. Abbasgholipour, B. Mohammadi Alasti, V. Abbasgholipour, A. Derakhshan, M. Abbasgholipour, Sh. Rahmatfam, Sh. Rahmatfam and R. Habibifar, Image processing with genetic algorithm in a raisin sorting system based on machine vision- Proceedings, Fourth Int. Conf. Digital Image Process. (ICDIP 2012), Volume 8334, 83342N, 2012.
 S.N. Deepa and D. Rasi, Global biotic cross-pollination algorithm enhanced with evolutionary strategies for color image segmentation, Soft Comput. 23 (2019), 2545–2559.
 C.Q. Duan, Y. Shi, B.Q. Zhu, H.U. Javed and J. Wang, Free and glycosidically bound volatile compounds in sundried raisins made from different fragrance intensities grape varieties using a validated HS-SPME with GC–MS method, Food Chem. 228 (2017), 125–135.
 K. Gupta and K. Chopra, Genetic algorithm-a literature review, Int. Conf. Machine Learn. Big Data Cloud Parallel Comput. (COMITCon), 2019, pp. 14–16.
 R. Jalili-Marandi, Small Fruits, (In Farsi), Jahad Daneshgahi Urmia. Urmia, Iran, 2008.
 S. KangshengLiu, X. Yu, D. Wu and Y. He, Application of hybrid imagefeatures for fast and non-invasive classification of raisin, J. Food Engine. 109 (2012), no. 3, 531–537.
 M. Khojastehnazhanda and H. Ramezanib, Machine vision system for classification of bulk raisins using texture features, J. Food Engin. 271 (2020).
 W. Kong, C. Zhang, F. Cao, F. Liu, S. Luo, Y. Tang and Y. He, Detection of sclerotinia stem Rot on oilseed rape (Brassica napus L.) leaves using hyperspectral imaging, Sensors 18 (2018).
 M.A. Laribi, A. Mlika, L. Romdhane and S. Zeghloul, A combined geneticalgorithm-fuzzy logic method (GA-FL) in mechanisms synthesis, Mech. Machine Theory 39 (2004), 717–735.
 H. Lee, M.S. Kim, Y.R. Song, C.S. Oh, H.S. Lim, W.H. Lee, J.S. Kang and B.K. Cho, Non-destructive evaluation of bacteria-infected watermelon seeds using visible/near-infrared hyperspectral imaging, J. Sci. Food Agric. 97 (2016).
 X. Li and X, Liu, Detection level of raisins based on neural network and digital image, Third Pacific-Asia Conf. Circuits Commun. Syst. (PACCS), 2011, pp. 17–18.
 S. Mushai, A. Akbari and M. Pahlavani, The effect of red currency fluctuations on Iran’s raisin export, Sci. Res. Quart. Agricul. Econ. Dev. 28 (2013), no. 2.
 S. Naderi, M. Ghasemi Nejad and R. MortezaTaki, Measuring the energy and environmental indices for apple (production and storage) by life cycle assessment (case study: Semirom county, Isfahan, Iran), Envir. Sustain. Indicators 6 (2020), 100034.
 A.O. Omolola, A.I.O. Jideani and P.F. Kapila, Quality properties of fruits as affected by drying operation, Crit. Rev. Food Sci. Nutr. 57 (2017), 95–108.
 S.P. Pawar and A. Sarkar, Cost effective grading process for grape raisins based on HSI and fuzzy logic algorithms, Int. J. Comput. Appl. 67 (2013), no. 22.
 S.P. Sydlow, Agricultural products processing equipment engineering, Course notes, Tabriz University, 2011.
 D. Wang, C.Q. Duan, Y. Shi, B. Zhu, H.U. Javed and J. Wang, Free and glycosidically bound volatile compounds in sun-dried raisins made from different fragrance intensities grape varieties using a validated HS-SPME with GC–MS method, Food Chem. 228 (2017), 125–135.
 Y. Zhao, M.L. Guindo, X. Xiang Shi, M. Sun and Y. He, A novel raisin segmentation algorithm based on deep learning and morphological analysis, Engenharia Agr´ıcola 39 (2019), 639–648.
 Y. Zhao, C. Zhang, S. Zhu, P. Gao, L. Feng and Y. He, Non-Destructive and rapid variety discrimination and visualization of single grape seed using near-infrared hyperspectral imaging technique and multivariate analysis, Molecules 23 (2018).
 J.P. Zoffoli, B.A. Latorre and P. Naranjo, Preharvest applications of growth regulators and their effect on postharvest quality of table grapes during cold storage, Postharvest Bio. Technol. 51 (2009), 183–192.