Utilizing machine vision and artificial neural networks for dried grape sorting during production
Original scientific paper
DOI:
https://doi.org/10.2298/CICEQ231003030RKeywords:
Machine vision, grape drying process, artificial neural network, embedded systemsAbstract
This study introduces a machine vision technique that utilizes an artificial neural network (ANN) to develop a predictive model for classifying dried grapes during the drying process. The primary objective of this model is to mitigate the burden placed on the operator and minimize the occurrence of over-dried items. The present study involves the development of a model that is constructed using the characteristics of grape color and shape. There exist two distinct categories of labels for grapes: fully desiccated grapes, commonly referred to as raisins, and grapes that have undergone partial drying. Image processing is utilized to collect and observe five significant characteristics of grapes during the drying process. The findings indicate a significant decrease in the levels of red, green, and blue colors (RGB) during the initial 15-hour drying period. The predictive model extracts properties such as RGB color, roundness, and shrinkage from the image while it undergoes the drying process. The artificial neural network (ANN) model achieved a level of accuracy performance of 78%. In this work, the dehydration apparatus will cease operation in an automated manner whenever the entirety of the grapes situated on the tray have been projected to undergo the transformation into raisins.
References
L.M. Donini, S. Claudia, C. Carlo, Arch. Gerontol. Geriatr. 49 (2009) 61–69. https://doi.org/10.1016/j.archger.2009.09.013.
R. Megías-Pérez, J. Gamboa-Santos, A.C. Soria, M. Villamiel, A. Montilla, Food Chem. 150 (2014) 41–48. https://doi.org/10.1016/j.foodchem.2013.10.141.
P. Ruangurai, C. Silawatchananai, S. Howimanporn, PSO based Fictitious Reference Iterative Tuning of air dryer for fruit drying process, in Proceeding of 2020 9th International Congress on Advanced Applied Informatics (IIAI-AAI), Kitakyushu, Japan (2020), p. 548. https://doi.org/10.1109/IIAI-AAI50415.2020.00114.
A. Dai, X. Zhou, X. Liu, IEEE Access 5 (2017) 14981–14990. https://doi.org/10.1109/ACCESS.2017.2733760.
S. Karaaslan, K. Ekinci, D. Akbolat, Infrastrukt. Ekolog. Terenów Wiejskich IV/1 (2017) 1317–1327. https://doi.org/10.14597/infraeco.2017.4.1.101.
C. Carter, W. Shaojin, E.P. Vincent, J. Food Sci. 70 (2005) E344–E349. https://doi.org/10.1111/j.1365-2621.2005.tb09975.x
J.O. Ojediran, C.E. Okonkwo, A.J. Adeyi, O. Adeyi, A.F. Olaniran, N.E. George, A.T. Olayanju, Heliyon 6 (2020) E03555. https://doi.org/10.1016/j.heliyon.2020.e03555.
Y. Pei, Z. Li, C. Ling, L. Jiang, X. Wu, C. Song, J. Li, F. Song, W. Xu, Food Measure. Charact. 16 (2022) 3815–3831. https://doi.org/10.1007/s11694-022-01453-8.
P. Ruangurai, T. Nachan, R. Bunchan, C. Silawatchananai, Monitoring characteristics of dried banana slices using image processing, in Proceeding of 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology: Smart Electrical System and Technology, Chiang Mai, Thailand (2021), p. 148. https://doi.org/10.1109/ECTI-CON51831.2021.9454907.
N. Behroozi Khazaei, T. Tavakoli, H. Ghassemian, M.H. Khoshtaghaza, A. Banakar, Comput. Electron. Agric. 98 (2013) 205–213. https://doi.org/10.1016/j.compag.2013.08.010.
R. Subramanyam, M. Narayanan, Chem. Ind. Chem. Eng. Q. 29 (2023) 87-98. https://doi.org/10.2298/CICEQ220106017S.
E. Ropelewska, D.E. Kruczyńska, A.M. Rady, K.P. Rutkowski, D. Konopacka, K. Celejewska, M. Mieszczakowska-Frąc, Agriculture 13 (2023) 562. https://doi.org/10.3390/agriculture13030562.
I. Cinar, M. Koklu, S. Tasdemir, Gazi J. Eng. Sci. 6 (2020) 200–209. https://doi.org/10.30855/gmbd.2020.03.03.
K. Przybyl, A. Duda, K. Koszela, J. Stangierski, M. Polarczyk, L. Gierz, Sensors 20 (2020) 499. https://doi.org/10.3390/s20020499.
M. Sabzevari, N. Behroozi‐Khazaei, H. Darvishi, J. Food Process Eng. 44 (2021) e13796. https://doi.org/10.1111/jfpe.13796.
A. Patidar, S. Vishwakarma, D. Meena, Food Front 2 (2021) 46-61. https://doi.org/10.1002/fft2.64.
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Copyright (c) 2023 Piyanun Ruangurai, Nattabut Tanasansurapong, Sirakupt Prasitsanha, Rewat Bunchan, Wiput Tuvayanond3, Thana Chotchuangchutchaval, Chaiyaporn Silawatchananai
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King Mongkut's University of Technology North Bangkok
Grant numbers KMUTNB-65-BASIC-32
