Classification of hazelnut varieties by using artificial neural network and discriminant analysis

Keywords: Corylus avellana, artificial intelligence, multivariate statistical methods


Aim of study: This study was conducted to classify hazelnut (Corylus avellana L.) varieties by using artificial neural network and discriminant analysis.

Area of study: Samsun Province, Turkey.

Material and methods: The physical, mechanical and optical properties of 11 hazelnut varieties were determined for three major axes. The parameters of physical, mechanical and optical properties were included as independent variables, while hazelnut varieties were included as dependent variables. Models were created for each of the three axes to classify hazelnut varieties.

Main results: Classification success rates with Artificial Neural Networks (ANN) and Discriminant Analysis (DA) were found as 89.1% and 92.7% for X axis, as 92.7% and 92.7% for Y axis and as 86.8% and 88.7% for Z axis, respectively. The classification results of ANN and DA models were found to be very close to each other. Both models can be used in the classification of hazelnut varieties.

Research highlights: The results obtained for the identification and classification of hazelnut varieties show the feasibility and effectiveness of the proposed models.


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Ağın O, Taner A, 2015. Determination of weed intensity in wheat production using image processing techniques. Anadolu J Agr Sci 30: 110-117.

Alasavar C, Shahidi F, Liyanapathirana CM, Oshima T, 2003. Turkish Tombul hazelnut (Corylus avellana L.) 1. Compositional characteristics. J Agr Food Chem 51: 3790-3796.

Alpar R, 2017. Applied multivariate statistical. Detay Yayıncılık. Ankara, Turkey, 820 pp.

Aydın C, 2002. Physical properties of hazelnuts. Biosyst Eng 82(3): 297-303.

Azizi A, Abbaspour-Gilandeh Y, Nooshyar M, Afkari-Sayah A, 2016. Identifying potato varieties using machine vision and artificial neural networks. Int J Food Prop 19: 618-635.

Beyer M, Hahn R, Peschel S, Harz M, Knoche M, 2002. Analysing fruit shape in sweet cherry (Prunus avium L.). Sci Hortic 96: 139-150.

Bishop CM, 1995. Neural network for pattern recognition. Clarendon, Oxford.

Brewer MT, Moyseenko JB, Monforte AJ, van der Knaap E, 2007. Morphological variation in tomato: a comprehensive study of quantitative trait loci controlling fruit shape and development. J Exp Bot 58(6): 1339-1349.

Cannon CH, Manos PS, 2001. Combining and comparing morphometric shape descriptors with a molecular phylogeny: the case of fruit type evolution in Bornean Lithocarpus (Fagaceae). Syst Biol 50(6): 860-880.

Chen X, Xun Y, Li W, Zhang J, 2010. Combining discriminant analysis and neural networks for corn variety identification, Comput Electron Agr 71: 48-53.

Demirbaş HY, Dursun İ, 2007. Determination of some physical properties of wheat grains by using image analysis. J Agr Sci 13(3): 176-185.

Dubey BP, Bhagwat SG, Shouche SP, Sainis JK, 2006. Potential of artificial neural networks in varietal identification using morphometry of wheat grains. Biosyst Eng 95: 61-67.

Fallico B, Arena E, Zappala M, 2003. Roasting of hazelnuts. Role of oil in colour development and hydroxy methyl furfural formation. Food Chem 81: 569-573.

FAOSTAT, 2018. Classifications and standards. Food and Agriculture Organization of the United Nations, Rome.

Ford MG, Pitt WR, Whitley DC, 2004. Selecting compounds for focused screening using linear discriminant analysis and artificial neural networks. J Mol Graph Model 22: 467-472.

Francisco EL, Jeffrey KB, Amarat HS, Anne P, Elizabeth AB, Jinhe B, Elena L, 2021. Color biogenesis data of tomatoes treated with hot-water and high temperature ethylene treatments. Data in Brief 36: 107123.

Gonzalez RC, Woods RE, 2008, Digital image processing, Pearson Int Ed, Pearson Prentice Hall, USA. ISBN: 0-13-168728-x 978-0-13-168728-8.

Guiné RPF, Almeida CFF, Correia PMR, Mendes M, 2015. Modelling the influence of origin, packing and storage on water activity, colour and texture of almonds, hazelnuts and walnuts using artificial neural networks. Food Bioprocess Technol 8(5): 1113-1125.

Güzeller CO, 2016. Multivariate statistics for everyone. Maya Akademi Yayıncılık, Ankara, Turkey, 346 pp.

Jacobs RA, 1988. Increased rate of convergence through learning rate adaptation. Neural Networks 1(4): 295-307.

Jain RK, Bal S, 1997. Properties of pearl millet. J Agr Eng Res 66(2): 85-91.

Kalaycı Ş, 2018. SPSS Applied multivariate statistics techniques, 9th ed, Dinamik Akademi Yayıncılık, Turkey, 426 pp.

Kays SJ, 1999. Preharvest factors affecting appearance. Postharv Biol Technol 15: 233-247.

Levenberg K, 1944. A method for the solution of certain nonlinear problems in least squares. Quart Appl Math 2: 164-168.

Marquardt DW, 1963. An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11: 431-441.

Menesatti P, Costa C, Paglia G, Pallottino F, D'Andrea S, Rimatori V, Aguzzic J, 2008. Shape-based methodology for multivariate discrimination among Italian hazelnut cultivars. Biosyst Eng 101: 417-424.

Minai AA, Williams RD, 1990. Back-propagation heuristics: a study of the extended Delta-bar-delta algorithm. Int Joint Conf on Neural Networks, vol.1, pp: 595-600, San Diego, CA, USA.

Mohsenin NN, 1970. Physical properties of plant and animal materials. Gordon & Breach Sci Publ Inc, NY.

Oddone M, Aceto M, Baldizzone M, Musso D, Osella D, 2009. Authentication and traceability study of hazelnuts from Piedmont, Italy. J Agr Food Chem 57(9): 3404-3408.

Özdamar K, 2004. Statistical data analysis with package programs (Multivariate analysis), Kaan Kitabevi, Eskişehir, Turkey, 502 pp.

Parcerisa J, Richardson DG, Rafecas M, Codoni R, Boatella S, 1998. Fatty acid, to cophero land sterol content of some hazelnut varieties (Corylus avellana L.) harvested in Oregon (USA). J Chromat 805: 259-268.

Pourreza A, Pourreza H, Abbaspour-Fard MH, Sadrnia H, 2012. Identification of nine Iranian wheat seed varieties by textural analysis with image processing. Comput Electron Agr 83: 102-108.

Purushothaman S, Srinivasa YG, 1994. A back-propagation algorithm applied to tool wear monitoring. Int J Machin Tools Manufact 34(5): 625-631.

Sharma S, 1996. Applied multivariate techniques. John Wiley & Sons Inc., Canada.

Tabachnick BG, Fidell LS, 2007. Using multivariate statistics, 5th ed. Harber Collins Pub., London, 980 pp.

Tabatabaeefar A, 2003. Moisture-dependent physical properties of wheat. Int Agrophys 17: 207-211.

Taner A, Gültekin SS, Çarman K, 2010. Prediction of the parameters radial centrifugal pumps with artificial neural networks. Selcuk J Agr Food Sci 24(1): 28-38.

Taner A, Öztekin YB, Tekgüler A, Sauk H, Duran H, 2018. Classification of varieties of grain species by artificial neural networks. Agronomy 8: 123.

Taner A. Öztekin YB, Duran H, 2021. Performance analysis of deep learning CNN models for variety classification in hazelnut. Sustainability 13: 6527.

Visen NS, Paliwal J, Jayas DS, White NDG, 2002. Specialist neural networks for cereal grain classification. Biosyst Eng 82: 151-159.

Yang CC, Prasher SO, Landry JA, Ramaswamy HS, 2003. Development of a herbicide application map using artificial neural networks and fuzzy logic. Agr Syst 76(2): 561-574.

How to Cite
KelesO., & TanerA. (2021). Classification of hazelnut varieties by using artificial neural network and discriminant analysis . Spanish Journal of Agricultural Research, 19(4), e0211.
Agricultural engineering