پیوندهای مفید
آمار
| تعداد نشریات | 22 |
| تعداد شمارهها | 705 |
| تعداد مقالات | 10,146 |
| تعداد مشاهده مقاله | 71,454,819 |
| تعداد دریافت فایل اصل مقاله | 63,176,396 |
Optimization of raisin sorting machine using genetic algorithm | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 24، دوره 14، شماره 2، اردیبهشت 2023، صفحه 301-312 اصل مقاله (1.23 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.27919.3757 | ||
| نویسندگان | ||
| Seyed Hamed Mirkhorasani؛ Mehdi Abasgholipour* ؛ Behzad Mohammadi Alasti | ||
| Department of Bio-System Mechanics, Bonab Branch, Islamic Azad University, Bonab, Iran | ||
| تاریخ دریافت: 26 فروردین 1401، تاریخ بازنگری: 04 مرداد 1401، تاریخ پذیرش: 08 مرداد 1401 | ||
| چکیده | ||
| Sorting agricultural products refers to grading food and other crops based on size, color, appearance, and other factors such as separating impurities, fruits, and damaged and rotten products. Today, sorting technology and related equipment for grading agricultural crops are progressing in developed countries, which can be found in most large agricultural units. Therefore, initial packaging and transportation of the product are facilitated, and more added value can be provided for farmers. This study aimed to optimize the raisin sorting machine based on a genetic algorithm to increase the quality of raisin grading. Therefore, a seedless white variety of grape samples were randomly selected and prepared from an orchard in Makan, East Azerbaijan, Iran. Digital image processing techniques such as the image processing toolbox in MATLAB were used to extract features from an image for sorting. Other meta-heuristic algorithms such as PSO, differential evolution, and artificial bee colony algorithm were used to evaluate the accuracy of the results. According to the results, the artificial bee colony algorithm had better accuracy than other algorithms, but the convergence speed was lower, and the computational volume was higher. However, the genetic and PSO algorithms had an accuracy almost equal to the artificial bee colony algorithm despite having a higher speed of convergence and lower computational operations, which can be used as the best algorithm in this application. Differential evolutionary algorithms and harmony search require processing in many iterations, and the computation time is not economical. Therefore, the clustering of raisins in industrial units requires high clustering speed and minimum error to avoid discarding or outliers, and genetics and PSO algorithms were acceptable. | ||
| کلیدواژهها | ||
| Agricultural crops؛ Sorting؛ Raisins؛ Genetic algorithm؛ Image processing | ||
| مراجع | ||
|
[1] 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.
[2] 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.
[3] 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.
[4] K. Gupta and K. Chopra, Genetic algorithm-a literature review, Int. Conf. Machine Learn. Big Data Cloud Parallel Comput. (COMITCon), 2019, pp. 14–16.
[5] R. Jalili-Marandi, Small Fruits, (In Farsi), Jahad Daneshgahi Urmia. Urmia, Iran, 2008.
[6] 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.
[7] M. Khojastehnazhanda and H. Ramezanib, Machine vision system for classification of bulk raisins using texture features, J. Food Engin. 271 (2020). [8] 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). [9] 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.
[10] 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).
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] S.P. Sydlow, Agricultural products processing equipment engineering, Course notes, Tabriz University, 2011.
[17] 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.
[18] 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.
[19] 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).
[20] 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. | ||
|
آمار تعداد مشاهده مقاله: 17,161 تعداد دریافت فایل اصل مقاله: 10,774 |
||