Analysis of Computer Vision Algorithms to Determine the Quality of Fermented Cocoa (Theobroma Cacao): Systematic Literature Review

  • Karen Mite-BaidalEmail author
  • Evelyn Solís-Avilés
  • Tayron Martínez-Carriel
  • Augusto Marcillo-Plaza
  • Elicia Cruz-Ibarra
  • Wilmer Baque-Bustamante
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 901)


Computer vision techniques have been used for the automation of processes in the agricultural sector due to the benefits obtained such as effectiveness and quality. A clear example is the analysis of cocoa beans quality. The increasing interest of computer vision in this area calls for a clear, systematic overview. In this sense, we present a systematic literature review (SLR) of computer vision algorithms to determine the quality of fermented cocoa in a six-year period: from 2013–2018. The aim of this review is to identify the techniques or computer vision algorithms used to assess fermentation index of cocoa beans for quality control, as well, the main physical and chemical characteristics of the cocoa beans identified through the computer vision algorithms. The results show that the PLS (Partial Least-Squares) algorithm is the most used for the classification of images in a statistical approach. Also, color is the physical parameter that is commonly identified through artificial vision algorithms. Meanwhile, Fat and pH are the chemical parameters most identified by FT-NIR (Fourier transform near-infrared) technology in conjunction with the chemometric technique.


Computer vision Computer vision algorithms Image recognition 



We thank the researchers of the Agrarian University of Ecuador for their contribution in the search of the opportune information for the literature review


  1. 1.
    Humston, E.M., Knowles, J.D., McShea, A., Synovec, R.E.: Quantitative assessment of moisture damage for cacao bean quality using two-dimensional gas chromatography combined with time-of-flight mass spectrometry and chemometrics. J. Chromatogr. A 1217, 1963–1970 (2010)CrossRefGoogle Scholar
  2. 2.
    Arefi, A., Motlagh, A.M., Khoshroo, A.: Recognition of weed seed species by image processing. J. Food Agric. Environ. 9, 379–383 (2011)Google Scholar
  3. 3.
    Giraldo-Zuluaga, J.-H., Salazar, A., Daza, J.M.: Semi-Supervised Recognition of the Diploglossus Millepunctatus Lizard Species using Artificial Vision Algorithms. (2016)Google Scholar
  4. 4.
    Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, London (2010)zbMATHGoogle Scholar
  5. 5.
    Liu, D., Zeng, X.-A., Sun, D.-W.: Recent developments and applications of hyperspectral imaging for quality evaluation of agricultural products: a review. Crit. Rev. Food Sci. Nutr. 55, 1744–1757 (2015)CrossRefGoogle Scholar
  6. 6.
    Unay, D., Gosselin, B., Kleynen, O., Leemans, V., Destain, M.F., Debeir, O.: Automatic grading of Bi-colored apples by multispectral machine vision. Comput. Electron. Agric. 75, 204–212 (2011)CrossRefGoogle Scholar
  7. 7.
    Muñoz, F.I.I., Comport, A.I.: Point-to-hyperplane RGB-D pose estimation: fusing photometric and geometric measurements. In: IEEE International Conference on Intelligent Robots System 2016 Nov 24–29 (2016)Google Scholar
  8. 8.
    Zhang, B., et al.: Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Res. Int. 62, 326–343 (2014)CrossRefGoogle Scholar
  9. 9.
    León-Roque, N., Abderrahim, M., Nuñez-Alejos, L., Arribas, S.M., Condezo-Hoyos, L.: Prediction of fermentation index of cocoa beans (Theobroma cacao L.) based on color measurement and artificial neural networks. Talanta 161, 31–39 (2016)CrossRefGoogle Scholar
  10. 10.
    Teye, E., Huang, X.: Novel prediction of total fat content in cocoa beans by FT-NIR Spectroscopy based on effective spectral selection multivariate regression. Food Anal. Methods 8, 945–953 (2015)CrossRefGoogle Scholar
  11. 11.
    Hue, C., et al.: Near infrared spectroscopy as a new tool to determine cocoa fermentation levels through ammonia nitrogen quantification. Food Chem. 148, 240–245 (2014)CrossRefGoogle Scholar
  12. 12.
    Teye, E., et al.: Estimating cocoa bean parameters by FT-NIRS and chemometrics analysis. Food Chem. 176, 403–410 (2015)CrossRefGoogle Scholar
  13. 13.
    Sunoj, S., Igathinathane, C., Visvanathan, R.: Nondestructive determination of cocoa bean quality using FT-NIR spectroscopy. Comput. Electron. Agric. 124, 234–242 (2016)CrossRefGoogle Scholar
  14. 14.
    Armin, L., Adhitya, Y.: Classifying physical morphology of cocoa beans digital images using multiclass ensemble least-squares support vector machine classifying physical morphology of cocoa beans digital images using multiclass ensemble least-squares support vector machine. J. Phys: Conf. Ser. 979, 10 (2018)Google Scholar
  15. 15.
    Astika, I.W., Solahudin, M., Kurniawan, A., Wulandari, Y.: Determination of cocoa bean quality with image processing and artificial neural network. AFITA 2010 - Comput. Based Data Acquis. Control Agric. 2760, 6 (2013)Google Scholar
  16. 16.
    Soto, J., Granda, G., Prieto, F., Ipanaque, W., Machacuay, J.: Cocoa bean quality assessment by using hyperspectral images and fuzzy logic techniques. Twelfth Int. Conf. Qual. Control Artif. Vis. 9534, 1–7 (2015)Google Scholar
  17. 17.
    Ochoa, D., Criollo, R., Liao, W., Cevallos-Cevallos, J., Castro, R., Bayona, O.: Improving the detection of cocoa bean fermentation-related changes using image fusion. Proc. SPIE - Int. Soc. Opt. Eng. 10198, 1–6 (2017)Google Scholar
  18. 18.
    Ruiz Reyes, J., Soto Bohórquez, J., Ipanaqué Alama, W.: Hyperspectral analysis based anthocyanin index (ARI2) during cocoa bean fermentation process. Proc. - 2015 Asia-Pacific Conf. Comput. Syst. Eng. APCASE 2015 2, 169–172 (2015)Google Scholar
  19. 19.
    Veites-campos, S.A., Ramírez-betancour, R.: Identification of cocoa pods with image processing and artificial neural networks. Int. J. Adv. Eng. Manag. Sci. 4, 510–518 (2018)CrossRefGoogle Scholar
  20. 20.
    Hashimoto, J.C., et al.: Quality control of commercial cocoa beans (Theobroma cacao L.) by near-infrared spectroscopy. Food Anal. Methods 11, 1510–1517 (2018)CrossRefGoogle Scholar
  21. 21.
    Ruiz Reyes, J.M., Soto Bohorquez, J., Ipanaque, W.: Evaluation of spectral relation indexes of the Peruvians cocoa beans during fermentation process. IEEE Lat. Am. Trans. 14, 2862–2867 (2016)CrossRefGoogle Scholar
  22. 22.
    Kutsanedzie, F.Y.H., Chen, Q., Hassan, M.M., Yang, M., Sun, H., Rahman, M.H.: Near infrared system coupled chemometric algorithms for enumeration of total fungi count in cocoa beans neat solution. Food Chem. 240, 231–238 (2018)CrossRefGoogle Scholar
  23. 23.
    Huang, X., Teye, E., Sam-Amoah, L.K., Han, F., Yao, L., Tchabo, W.: Rapid measurement of total polyphenols content in cocoa beans by data fusion of NIR spectroscopy and electronic tongue. Anal. Methods 6, 5008–5015 (2014)CrossRefGoogle Scholar
  24. 24.
    Kutsanedzie, F.Y.H., Chen, Q., Sun, H., Cheng, W.: In situ cocoa beans quality grading by near-infrared-chemodyes systems. Anal. Methods 9, 5455–5463 (2017)CrossRefGoogle Scholar
  25. 25.
    Jimenez, J.C., et al.: Differentiation of Ecuadorian National and CCN-51 cocoa beans and their mixtures by computer vision. J. Sci. Food Agric. 98, 2824–2829 (2018)CrossRefGoogle Scholar
  26. 26.
    Bedini, A., Zanolli, V., Zanardi, S., Bersellini, U., Dalcanale, E., Suman, M.: Rapid and simultaneous analysis of xanthines and polyphenols as bitter taste markers in bakery products by FT-NIR spectroscopy. Food Anal. Methods 6, 17–27 (2013)CrossRefGoogle Scholar
  27. 27.
    Lombaert, S.De, Laurent, J., Lehon, M.: Profile of cacao cultivated in Colombia: a study based on standardized methods, indicators of quality and variety. Int. J. Food Nutr. Res. 2, 1–3 (2018)Google Scholar
  28. 28.
    Hasegawa, R., Hotta, K.: Stacked partial least squares regression for image classification. In: 2015 3rd IAPR Asian Conference on Pattern Recognit, pp. 765–769 (2015)Google Scholar
  29. 29.
    Huang, K., Li, S., Kang, X., Fang, L.: Spectral-Spatial Hyperspectral Image Classification Based on KNN. Sens. Imaging. 17, 1–13 (2016)CrossRefGoogle Scholar
  30. 30.
    Yu, S., Jia, S., Xu, C.: Convolutional neural networks for hyperspectral image classification. Neurocomputing 219, 88–98 (2017)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Agricultural Sciences, Computer Science DepartmentAgrarian University of EcuadorGuayaquilEcuador

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