International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.6, No.5, Apr. 2014

Artificial Neural Networks in Fruits: A Comprehensive Review

Full Text (PDF, 305KB), PP.53-63

Views:139   Downloads:15


Sumit Goyal

Index Terms

Artificial neural networks (ANN);machine learning;backpropagation;fruits; neurocomputing;soft computing


This review discusses the application of artificial neural networks (ANN) modeling in fruits. It covers all fruits in which ANN modeling has been applied. ANN is quite a new and easy computational modeling approach used for prediction, which has become popular and accepted by food industry, researchers, scientists and students. ANNs have been applied in almost every field of science and technology, viz., speech synthesis & recognition, pattern classification, adaptive interfaces between humans & complex physical systems, clustering, function approximation, image data compression, non-linear system modeling, associative memory, combinatorial optimization, control and several others, as they have proved valuable tools for obtaining the required output. ANN provides an exciting alternative method for solving a variety of problems in different areas of science and engineering. The aim of this communication is to discover the recent advances of ANN technology implemented in fruits, and discuss the critical role that ANN plays in predictive modelling.

Cite This Paper

Sumit Goyal,"Artificial Neural Networks in Fruits: A Comprehensive Review", IJIGSP, vol.6, no.5, pp.53-63, 2014.DOI: 10.5815/ijigsp.2014.05.07


[1]Y. Huang, L.J. Kangas and B.A. Rasco, “Applications of artificial neural networks (ANNs) in food science,” Crit. Rev Food Sci. Nutr., Vol. 47, No.2, pp.133-126, 2007.

[2]Sumit Goyal, “Artificial neural networks in vegetables: A comprehensive review,” Sci. J. of Crop Sci., Vol.2, No.7, pp. 75-94, 2013.

[3] Sumit Goyal, “Artificial neural networks (ANNs) in food science – A review,” Int. J. of Sci. World, Vol.1, No.2, pp.19-28, 2013.

[4]Sumit Goyal, “Predicting properties of cereals using artificial neural networks: A review,” Sci. J. of Crop Sci., Vol.2, No.7, pp. 95-115, 2013.

[5]Sumit Goyal and G.K. Goyal, “Machine learning cascade algorithm for analyzing shelf life of processed cheese,” VAWKUM Trans. on Comput. Sci., Vol.2, No.1, pp.1-6, 2013.

[6]G.K Goyal and Sumit Goyal, “Cascade artificial neural network models for predicting shelf life of processed cheese,” J. of Adv. in Info. Tech., Vol.4, No.2, pp.80-83, 2013.

[7]M. Bhotmange and P. Shastri, “(Eds.) Application of artificial neural networks to food and fermentation technology,” ANN – Indus. and Con. Eng. App., Prof. Kenji Suzuki, ISBN: 978-953-307-220-3, InTech, 2011.

[8]Sumit Goyal and G.K. Goyal, “. Artificial neural networks for dairy industry: A Review,” J. of Adv. Comp. Sci. & Tech. Vol.1, No.3, pp.101-115, 2012.

[9]A. Khoshhal, A.A. Dakhel, A. Etemadi and S. Zereshki, “Artificial neural network modeling of apple drying process,” J. of Food Process Eng., Vol.33, pp.298–313, 2010.

[10]N. Raharitsifa and C. Ratti, “Foam-mat freeze-drying of apple juice part 1: Experimental data and ANN simulations,” J. of Food Process Eng. Vol.33, pp.268–283, 2010.

[11]N. Maftoonazad, Y. Karimi, H.S. Ramaswamy and S.O. Prasher,“ Artificial neural network modeling of hyperspectral radiometric data for quality changes associated with avocados during storage,” J. of Food Process. and Preserv., Vol.35, No.4, pp.432–446, 2011.

[12]G.P. Parpinello, A. Fabbri, S. Domenichelli, V. Mesisca, L. Cavicchi and A. Versari, “Discrimination of apricot cultivars by gas multisensor array using an artificial neural network,” Biosys. Eng., Vol. 97, No.3, pp.371–378, 2007.

[13]E. Llobet, E.L. Hines, J.W. Gardner and S. Franco, “Non-destructive banana ripeness determination using a neural network-based electronic nose,” Measurement Sci. and Tech., Vol.10, No.6, pp.538–548, 1999.

[14]D. Jiménez, J. Cock, H.F. Satizábal, M.A. Barreto, A. Pérez-Uribe, A. Jarvis and P.V. Damme, “Analysis of Andean blackberry (Rubus glaucus) production models obtained by means of artificial neural networks exploiting information collected by small-scale growers in Colombia and publicly available meteorological data,” Comput. and Electron. in Agric., Vol.69, No.2, pp.198–208, 2009.

[15]R.K. Boccorh and A. Paterson, “An artificial neural network model for predicting flavour intensity in blackcurrant concentrates,” Food Qual. and Prefer., Vol.13, No.2, pp.117-128, 2002.

[16]C.R. Chen, H.S. Ramaswamy and I. Alli, “Prediction of quality changes during osmo-convective drying of blueberries using neural network models for process optimization,” Drying Tech.: An Int. J., Vol.19, No.3-4, pp.507-523, 2001.

[17]D. Guyer and X. Yang, “Use of genetic artificial neural networks and spectral imaging for defect detection on cherries. Comput. and Electron. in Agric., Vol.29, No.3, pp.179–194, 2000.

[18]Y.A. Ohali, “Computer vision based date fruit grading system: Design and implementation,” J. of King Saud Uni. – Comp. and Info. Sci., Vol.23, No.1, pp.29–36, 2011.

[19]Y. Saito, T. Hatanaka, K. Uosaki and K. Shigeto, “Eggplant classification using artificial neural network,” Proc. of the Int. Joint Conf. on Neural Netw., Vol.2, pp.1013- 1018, 2003.

[20]P. Rai, G.C. Majumdar, S. DasGupta and S. De, “Prediction of the viscosity of clarified fruit juice using artificial neural network: a combined effect of concentration and temperature,” J. of Food Eng., Vol.68, pp.527–533, 2005.

[21]H.L. Guo and L.X. Zhang, “Application of artificial neural network for classification of gooseberry species,” J. Agric. Mech. Res., Vol. 12, pp.195-198, 2006.

[22]L.J. Janik, D. Cozzolino, R. Dambergs, W. Cynkar and M. Gishen, “The prediction of total anthocyanin concentration in red-grape homogenates using visible-near-infrared spectroscopy and artificial neural networks,” Analytica Chimica Acta., Vol.594, No.1, pp.107–118, 2007.

[23]F. Mateo, R. Gadea, A′. Medina, R. Mateo and M. Jime′nez, “Predictive assessment of ochratoxin A accumulation in grape juice based-medium by Aspergillus carbonarius using neural networks,” J. of App. Microbio., Vol. 107, pp.915–927, 2009.

[24]Z. Wang, H. Duan, and C. Hu, “Modelling the respiration rate of guava (Psidium guajava L.) fruit using enzyme kinetics, chemical kinetics and artificial neural network,” Euro. Food Res. and Tech., Vol.229, No.3, pp.495-503, 2009.

[25]B.K. Bala, M.A. Ashraf, M.A. Uddin and S. Janjai, “Experimental and neural network prediction of the performance of a solar tunnel drier for drying jackfruit bulbs and leather,” J. of Food Process Eng., Vol.28, No.6, pp.552–566, 2005.

[26]S. Xudong, Z. Hailiang and L. Yande, “Nondestructive assessment of quality of Nanfeng mandarin fruit by a portable near infrared spectroscopy.” Int. J. of Agri. and Biological Eng., Vol.2, No.1, pp.65-71, 2009.

[27]J.A. Hernández-Pérez, M.A. Garc??a-Alvarado, G. Trystram and B. Heyd, “Neural networks for the heat and mass transfer prediction during drying of cassava and mango,” Innov. Food Sci. & Emerging Tech., Vol. 5, No.1, pp.57–64, 2004.

[28]Sutrisno, I.M. Edris and Sugiyono, “Quality prediction of mangosteen during storage using artificial neural network,” Int. Agric. Eng. Conf., Bangkok, Thailand, 7 – 10 December, 2009.

[29]C.Y. Cheok, N.L. Chin, Y.A. Yusof, R.A. Talib and C.L. Law, “Optimization of total phenolic content extracted from Garcinia mangostana Linn. hull using response surface methodology versus artificial neural network,” Industrial Crops and Prod. Vol.40, pp.247–253, 2012.

[30]P. Rai, G.C. Majumdar, S. DasGupta and S. De, “Modeling the performance of batch ultrafiltration of synthetic fruit juice and mosambi juice using artificial neural network,” J. of Food Eng., 71, 273–281, 2005.

[31]C.N. Thai, A.V.A. Resurreccion, G.G. Dull and D.A. Smittlle, “Modeling consumer preferences with neural networks,” Am. Soc. Agric. Eng. Pap. 907550. The Society: St. Joseph, MI, 1990.

[32]M. Kompany-Zareh, A. Massoumi and S. Pezeshk-Zadeh, “Simultaneous spectrophotometric determination of Fe and Ni with xylenol orange using principal component analysis and artificial neural networks in some industrial samples,” Talanta., Vol.48, No.2, pp.283–292, 1999.

[33]G.R. Chegini, J. Khazaei, B. Ghobadian and A.M. Goudarzi, “Prediction of process and product parameters in an orange juice spray dryer using artificial neural networks,” J. of Food Eng. 84(4), 534–543, 2008.

[34]Y. Ying, H. Jing, Y. Tao and N. Zhang, “Detecting stem and shape of pears using fourier transformation and an artificial neural network,” Am. Soc. of Agric. Eng., Vol.46, No.1, pp.157–162, 2003.

[35]R. Zhou and Y. Li, “Texture analysis of MR image for predicting the firmness of Huanghua pears (Pyrus pyrifolia Nakai, cv. Huanghua) during storage using an artificial neural network,” Magnetic Resonance Imaging., Vol.25, No.5, pp.727–732, 2007.

[36]S. Boonmung, B. Chomtee and K. Kanlayasiri, “Evaluation of artificial neural networks for pineapple grading,” J. of Texture Stu., Vol.37, No.5, pp.568–579, 2006.

[37]A. Motevali, S. Minaei, M.H. Khoshtaghaza, M. Kazemi and A.M. Nikbakht, “Drying of pomegranate arils: comparison of predictions from mathematical models and neural networks,” Int. J. of Food Eng., Vol.6, No.3, pp.1-20, 2010.

[38]S. Youssefi, Z. Emam-Djomeh and S.M. Mousavi, “Comparison of artificial neural network (ANN) and response surface methodology (RSM) in the prediction of quality parameters of spray-dried pomegranate juice,” Drying Tech.: An Int. J., Vol.27, No.(7-8), pp.910- 917, 2009.

[39]H. Huang and W.R. Li, “Application of artificial neural network (ANN) on the extraction of flavonoids from pomelo peel,” Hubei Agric. Sci., Vol.10, pp. 048, 2011.

[40]T. Morimoto, Y. Ouchi, M. Shimizu and M.S. Baloch, “Dynamic optimization of watering Satsuma mandarin using neural networks and genetic algorithms,” Agric. Water Manag., Vol.93, No.(1–2), pp. 1–10, 2007.

[41]J. Blasco, N. Aleixos, S. Cubero, J. Gómez-Sanchís and E. Moltó, “Automatic sorting of satsuma (Citrus unshiu) segments using computer vision and morphological features,” Comput. and Electron. in Agric., Vol.66, No.1, pp.1–8, 2009.

[42]M.Z. Abdullah, J. Mohamad-Saleh, A.S. Fathinul-Syahir and B.M.N. Mohd-Azemi, “Discrimination and classification of fresh-cut starfruits (Averrhoa carambola L.) using automated machine vision system,” J. of Food Eng., Vol.76, No.4, pp.506–523, 2006.

[43]L. Urruty, J.L. Giraudel, S. Lek, P. Roudeillac and M. Montury, “Assessment of strawberry aroma through SPME/GC and ANN methods, classification and discrimination of varieties,” J. of Agric. and Food Chem., Vol. 50, No.11, pp.3129–3136, 2002.

[44]V.D. Boishebert, L. Urruty, J.L. Giraudel and M. Montury, “Assessment of strawberry aroma through solid-phase microextraction?gas chromatography and artificial neuron network methods. Variety classification versus growing years,” J. of Agric. and Food Chem., Vol.52, No.9, pp. 2472–2478, 2004.

[45]K. Movagharnejad and M. Nikzad, “Modeling of tomato drying using artificial neural network,” Comput. and Electron. in Agric., Vol.59, pp. 78–85, 2007.

[46]X. Wang, M. Zhang, J. Zhu and S. Geng, “Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural network (ANN),” Int. J. of Remote Sensing., Vol.29, No.6, pp.1693-1706, 2008.

[47]P. Boonprasom and G. Bumroongitt, “Prediction of tangerine yield using artificial neural network (ANN),” CMU J., Vol4, No.1, pp. 39-48, 2005.

[48]T. Rithmanee, G. Bumroonggit and P. Boonprasom, “Quality prediction of 'sai nam pung' tangerine after truck transportation using artificial neural network. Acta Horticulturae , Vol.802, pp.379-384, 2008.

[49]S. Baki, Z.M. Annuar, I.M. Yassin, A.H. Hasliza, A. Zabidi, “Non-destructive classification of watermelon ripeness using mel-frequency cepstrum coefficients and multilayer perceptrons,” The IEEE Int. Joint Conf. on Neu. Netw., pp.1-6, 2010.