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Determination of ‘Hass’ avocado ripeness during storage by a smartphone camera using artificial neural network and support vector regression

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Abstract

Avocado undergoes quality transformation during storage, which needs to be managed in order to prevent quantity losses. A machine vision system devised with a smartphone camera was used to capture ‘Hass’ avocado images. Color features in L*a*b* and YUV (YUV color space is defined in terms of one luminance (Y) and two chrominance components (U and Y)) were extracted from the RGB images. Artificial Neural Network (ANN) and Support Vector Regression (SVR) were used compared for firmness estimation using the L*a*b* and YUV color features. The results indicated the ANN model is more accurate and robust than the SVR model for estimating ‘Hass’ avocado firmness with R2, RMSE, and RPD of 0.94, 0.38, 4.03 respectively for the model testing data set. It was concluded that the machine vision system devised with a smartphone camera and ANN model could be a low-cost tool for the determination of ripeness of ‘Hass’ avocado during harvest, storage, and distribution.

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References

  1. K.M. Alden, M. Omid, A. Rajabipour, B. Tajeddin, M.S. Firouz, Quality and shelf-life prediction of cauliflower under modified atmosphere packaging by using artificial neural networks and image processing. Comput. Electron. Agric. 163(6), 104861 (2019). https://doi.org/10.1016/j.compag.2019.104861

    Article  Google Scholar 

  2. S. Araghinejad, M. Azmi, M. Kholghi, Application of artificial neural network ensembles in probabilistic hydrological forecasting. J. Hydrol. 407(1–4), 94–104 (2011). https://doi.org/10.1016/j.jhydrol.2011.07.011

    Article  Google Scholar 

  3. H. Aravind, C. Rajgopal, K.P. Soman, A simple approach to clustering in excel. Int. J. Comput. Appl. 11(7), 19–25 (2010). https://doi.org/10.5120/1595-2144

    Article  Google Scholar 

  4. I. Arzate-Vázquez, J.J. Chanona-Pérez, M.J. Perea-Flores, G. Calderón-Domínguez, M.A. Moreno-Armendáriz, H. Calvo et al., Image processing applied to classification of avocado variety Hass (Persea americana Mill) during the ripening process. Food Bioprocess Technol. 4(5), 1307–1313 (2011). https://doi.org/10.1007/s11947-011-0595-6

    Article  Google Scholar 

  5. I.A. Basheer, M. Hajmeer, Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43(1), 3–31 (2000). https://doi.org/10.1016/S0167-7012(00)00201-3

    Article  CAS  PubMed  Google Scholar 

  6. A.K. Bhatt, D. Pant, Automatic apple grading model development based on back propagation neural network and machine vision, and its performance evaluation. AI Soc. 30(12), 45–56 (2013). https://doi.org/10.1007/s00146-013-0516-5

    Article  Google Scholar 

  7. K.A. Cox, T.K. McGhie, A. White, A.B. Woolf, Skin colour and pigment changes during ripening of ‘Hass’ avocado fruit. Postharvest Biol. Technol. 31(3), 287–294 (2004). https://doi.org/10.1016/j.postharvbio.2003.09.008

    Article  CAS  Google Scholar 

  8. E.O. Díaz, S. Kawamura, M. Matsuo, M. Kato, S. Koseki, Combined analysis of near-infrared spectra, colour, and physicochemical information of brown rice to develop accurate calibration models for determining amylose content. Food Chem. 286(15), 297–306 (2019). https://doi.org/10.1016/j.foodchem.2019.02.005

    Article  CAS  Google Scholar 

  9. H. Ding, Y.-W. Chin, A.D. Kinghorn, S.M. D’Ambrosio, Chemopreventive characteristics of avocado fruit. Semin. Cancer Biol. 17(5), 386–394 (2007). https://doi.org/10.1016/j.semcancer.2007.04.003

    Article  CAS  PubMed  Google Scholar 

  10. M. Dowlati, S.S. Mohtasebi, M. Omid, S.H. Razzavi, M. Jamzad, M. Guardia, Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes. J. Food Eng. 119(2), 277–287 (2013). https://doi.org/10.1016/j.jfoodeng.2013.05.023

    Article  Google Scholar 

  11. FAOSTAT. Food and Agriculture Organization of the United Nations, 2020. http://www.fao.org/faostat/en/#data/QC. Accessed June 21, 2020

  12. U. Flitsanov, A. Mizrach, A. Liberzon, M. Akerman, G. Zauberman, Measurement of avocado softening at various temperatures using ultrasound. Postharvest Biol. Technol. 20(3), 279–286 (2000). https://doi.org/10.1016/S0925-5214(00)00138-1

    Article  Google Scholar 

  13. N. Galili, I. Shmulevich, N. Benichou, Acoustic testing of avocado for fruit ripeness evaluation. Trans. ASAE 41(2), 399–407 (1998). https://doi.org/10.13031/2013.17164

    Article  Google Scholar 

  14. E.R. Guerrero, G.M. Benavides, Automated system for classifying hass avocados based on image processing techniques. 2014 IEEE Colombian Conference on Communications and Computing (COLCOM), Bogota, 2014, pp. 1–6. https://doi.org/10.1109/ColComCon.2014.6860414

  15. A. Gulli, S. Pal, Deep Learning with Keras (Packt Publishing Ltd., Birmingham, UK, 2017).

    Google Scholar 

  16. M.S. Howarth, I. Shmulevich, C. Raithatha, Y. Ioannides, Online non-destructive avocado firmness assessment based on low-mass impact technique. In Proceedings V World Avocado Congress (Actas V Congreso Mundial del Aguacate), 2003, pp. 679–685

  17. M. Hu, Q. Dong, B. Liu, P.K. Malakar, The potential of double k-means clustering for banana image segmentation. J. Food Process Eng. 37(1), 10–18 (2014). https://doi.org/10.1111/jifpe.12054

    Article  Google Scholar 

  18. Y. Karimi, N. Maftoonazad, H.S. Ramaswamy, S.O. Prasher, M. Marcotte, Application of hyperspectral technique for color classification avocados subjected to different treatments. Food Bioprocess Technol. 5(11), 252–264 (2012). https://doi.org/10.1007/s11947-009-0292-x

    Article  Google Scholar 

  19. C.E. Lewis, The maturity of avocados: a general review. J. Sci. Food Agric. 29(10), 866–875 (1978). https://doi.org/10.1002/jsfa.2740291007

    Article  Google Scholar 

  20. H. Li, H. He, Y. Wen, Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation. Optik 126(24), 4817–4822 (2015). https://doi.org/10.1016/j.ijleo.2015.09.127

    Article  Google Scholar 

  21. J. Li, W. Huang, C. Zhao, B. Zhang, A comparative study for the quantitative determination of soluble solids content, pH and firmness of pears by Vis/NIR spectroscopy. J. Food Eng. 116(2), 324–332 (2013). https://doi.org/10.1016/j.jfoodeng.2012.11.007

    Article  CAS  Google Scholar 

  22. N. Maftoonazad, H.S. Ramaswamy, Postharvest shelf-life extension of avocados using methyl cellulose-based coating. LWT-Food Sci. Technol. 38(6), 617–624 (2005). https://doi.org/10.1016/j.lwt.2004.08.007

    Article  CAS  Google Scholar 

  23. L.S. Magwaza, S.Z. Tesfay, A review of destructive and non-destructive methods for determining avocado fruit maturity. Food Bioprocess Technol. 8(8), 1995–2011 (2015). https://doi.org/10.1007/s11947-015-1568-y

    Article  Google Scholar 

  24. A. Mizrach, U. Flitsanov, Nondestructive ultrasonic determination of avocado softening process. J. Food Eng. 40(3), 139–144 (1999). https://doi.org/10.1016/S0260-8774(99)00038-2

    Article  Google Scholar 

  25. A. Mizrach, Determination of avocado and mango fruit properties by ultrasonic technique. Ultrasonics 38(1–8), 717–722 (2000). https://doi.org/10.1016/S0041-624X(99)00154-7

    Article  CAS  PubMed  Google Scholar 

  26. I.B. Mustaffa, S.F.B.M. Khairul, Identification of fruit size and maturity through fruit images using OpenCV-Python and Rasberry Pi. Proceedings of International Conference on Robotics, Automation and Sciences (ICORAS), 2017, pp. 1–3. https://doi.org/10.1109/ICORAS.2017.8308068

  27. H. Patel, R. Prajapati, M. Patel, Detection of quality in orange fruit image using SVM classifier. 2019 Third International Conference on Trends in Electronics and Informatics (ICOEI 2019), Tirunelveli, 2019, pp. 74–78. https://doi.org/10.1109/ICOEI.2019.8862758

  28. P.B. Pathare, U.L. Opara, F.A.-J. Al-Said, Colour measurement and analysis in fresh and processed foods: a review. Food Bioprocess Technol. 6(3), 36–60 (2013). https://doi.org/10.1007/s11947-012-0867-9

    Article  CAS  Google Scholar 

  29. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al., 2011. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12 (10), 2825–2830. https://arxiv.org/abs/1201.0490

  30. J.U. Porep, D.R. Kammerer, R. Carle, On-line application of near infrared (NIR) spectroscopy in food production. Trends Food Sci. Technol. 46(2), 211–230 (2015). https://doi.org/10.1016/j.tifs.2015.10.002

    Article  CAS  Google Scholar 

  31. N. Prieto, O. Pawluczyk, M.E.R. Dugan, J.L. Aalhus, A review of the principles and applications of near-infrared spectroscopy to characterize meat, fat, and meat products. Appl. Spectrosc. 71(7), 1403–1426 (2017). https://doi.org/10.1177/0003702817709299

    Article  CAS  PubMed  Google Scholar 

  32. G. Rateni, P. Dario, F. Cavallo, Smartphone-based food diagnostic technologies: a review. Sensors 17(6), 1453–1474 (2017). https://doi.org/10.3390/s17061453

    Article  Google Scholar 

  33. A. Sanaeifar, A. Bakhshipour, M. de la Guardia, Prediction of banana quality indices from color features using support vector regression. Talanta 148(12), 54–61 (2016). https://doi.org/10.1016/j.talanta.2015.10.073

    Article  CAS  PubMed  Google Scholar 

  34. F.M. Scott, B.C. Bystrom, E. Bowler, Persea Americana, mesocarp cell structure, light and electron microscope study. Bot. Gaz. 124(6), 423–428 (1963). https://doi.org/10.1016/336230

    Article  Google Scholar 

  35. C. Shu, D.H. Burn, Artificial neural network ensembles and their application in pooled flood frequency analysis. Water Resour. Res. 40(9), W09301 (2004). https://doi.org/10.1029/2003WR002816

    Article  Google Scholar 

  36. S.Z. Tesfay, L.S. Magwaza, N. Mbili, A. Mditshwa, Carboxyl methylcellulose (CMC) containing moringa plant extracts as new postharvest organic edible coating for avocado (Persea americana Mill) fruit. Sci. Hortic. 226(19), 201–207 (2017). https://doi.org/10.1016/j.scienta.2017.08.047

    Article  CAS  Google Scholar 

  37. L.C. Uzal, G.L. Grinblat, R. Namías, M.G. Larese, J.S. Bianchi, E.N. Morandi et al., Seed-per-pod estimation for plant breeding using deep learning. Comput. Electron. Agric. 150(3), 196–204 (2018). https://doi.org/10.1016/j.compag.2018.04.024

    Article  Google Scholar 

  38. S. Van der Walt, J.L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J.D. Warner, N. Yager et al., Scikit-image: image processing in Python. PeerJ 2, e453 (2014). https://doi.org/10.7717/peerj.453

    Article  PubMed  PubMed Central  Google Scholar 

  39. V. Vapnik, Statistical Learning Theory (Wiley, Inc., New York, 1998)

    Google Scholar 

  40. J.A. Villa-Rodríguez, F.J. Molina-Corral, J.F. Ayala-Zavala, G.I. Olivas, G.A. González-Aguilar, Effect of maturity stage on the content of fatty acids and antioxidant activity of ‘Hass’ avocado. Food Res. Int. 44(5), 1231–1237 (2011). https://doi.org/10.1016/j.foodres.2010.11.012

    Article  CAS  Google Scholar 

  41. A.B. Woolf, K.A. Cox, A. White, I.B. Ferguson, Low temperature conditioning treatments reduce external chilling injury of ‘Hass’ avocados. Postharvest Biol. Technol. 28(1), 113–122 (2003). https://doi.org/10.1016/S0925-5214(02)00178-3

    Article  Google Scholar 

  42. E.M. Yahia, G. Gonzalez-Aguilar, Use of passive and semi-active atmospheres to prolong the postharvest life of avocado fruit. LWT-Food Sci. Technol. 31(7–8), 602–606 (1998). https://doi.org/10.1006/fstl.1998.0383

    Article  CAS  Google Scholar 

  43. I. Zaier, C. Shu, T.B.M.J. Ouarda, O. Seidou, F. Chebana, Estimation of ice thickness on lakes using artificial neural network ensembles. J. Hydrol. 383(3–4), 330–340 (2010). https://doi.org/10.1016/j.jhydrol.2010.01.006

    Article  Google Scholar 

  44. H. Zhang, J. Wang, Evaluation of peach quality attribute using an electronic nose. J. Sens. Mater. 21(8), 419–431 (2009). https://doi.org/10.18494/SAM.2009.585

    Article  Google Scholar 

  45. H. Zhu, B. Chu, Y. Fan, X. Tao, W. Yin, Y. He, Hyperspectral imaging for predicting the internal quality of kiwifruits based on variable selection algorithms and chemometric models. Sci. Rep. 7(8), 7845 (2017). https://doi.org/10.1038/s41598-017-08509-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was supported by the grant for international students from the Tojuro Iijima Foundation for Food Science and Technology.

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  1. Graduate School of Agricultural Science, Hokkaido University, Kita-9, Nishi-9, Kita-ku, Sapporo, 060-8589, Japan

    Byeong-Hyo Cho, Kento Koyama & Shigenobu Koseki

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Correspondence to Shigenobu Koseki.

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Cho, BH., Koyama, K. & Koseki, S. Determination of ‘Hass’ avocado ripeness during storage by a smartphone camera using artificial neural network and support vector regression. Food Measure 15, 2021–2030 (2021). https://doi.org/10.1007/s11694-020-00793-7

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