Chestnut (Castanea sativa Mill.) cultivar classification: an artificial neural network approach

  • Yeşim Benal ÖZTEKİN Ondokuz Mayis University, Faculty of Agriculture, Department of Agricultural Machinery and Technologies Engineering, 55139, Samsun
  • Alper TANER Ondokuz Mayis University, Faculty of Agriculture, Department of Agricultural Machinery and Technologies Engineering, 55139, Samsun
  • Hüseyin DURAN Ondokuz Mayis University, Faculty of Agriculture, Department of Agricultural Machinery and Technologies Engineering, 55139, Samsun
Keywords: back propagation; chestnut classification; feed forward neural network; mechanical properties; physical properties; shape feature

Abstract

The present study investigated the possible use of artificial neural networks (ANN) to classify five chestnut (Castanea sativa Mill.) varieties. For chestnut classification, back-propagation neural networks were framed on the basis of physical and mechanical parameters. Seven physical and mechanical characteristics (geometric mean diameter, sphericity, volume of nut, surface area, shell thickness, shearing force and strength) of chestnut were determined. It was found that these characteristics were statistically different and could be used in the classification of species. In the developed ANN model, the design of the network is 7-(5-6)-1 and it consists of 7 input, 2 hidden and 1 output layers. Tansig transfer functions were used in both hidden layers, while linear transfer functions were used in the output layer. In ANN model, R2 value was obtained as 0.99999 and RMSE value was obtained as 0.000083 for training. For testing, R2 value was found as 0.99999 and RMSE value was found as 0.00031. In the approximation of values obtained with ANN model to the values measured, average error was found as 0.011%. It was found that the results found with ANN model were very compatible with the measured data. It was found that the ANN model obtained can classify chestnut varieties in a fast and reliable way.

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References

Arefi A, Motlagh AM, Teimourlou RF (2011). Wheat class identification using computer vision system and artificial neural networks. International Agrophysics 25(4):319-325.

Bağırkan Ş (1993). İstatistiksel Analiz [Statistical analysis]. Bilim Teknik Yayınevi. s. 301. İstanbul.

Bechtler H, Browne MW, Bansal PK, Kecman V (2001). New approach to dynamic modelling of vapour-compression liquid chillers: artificial neural networks. Applied Thermal Engineering 21:941-53.

Beyaz A, Özkaya,MT, İçen D (2017). Identification of some Spanish olive cultivars using image processing techniques. Scientia Horticulturae 225:286-292.

Beyaz A, Öztürk R (2016). Identification of olive cultivars using image processing techniques. Turkish Journal of Agriculture and Forestry 40:671-683.

Bounous G (2005). The chestnut: a multipurpose resource for the new millennium. Acta Horticulturae 693:33-40.

Bounous G, Beccaro GL, Barrel A, Lovisolo C (2001). Inventory of chestnut research, germplasm and references. FAO Regional Office for Europe, Rome, Italy 1-174.

Çarman K, Taner A (2012). Prediction of tire tractive performance by using artificial neural networks. Mathematical and Computational Applications 17(3):182-192.

Chen X, Xun Y, Li W, Zhang J (2010). Combining discriminant analysis and neural networks for corn variety identification. Computers and Electronics Agriculture 71:48-53.

Choudhary R, Paliwal J, Jayas DS (2008). Classification of cereal grains using wavelet, morphological, colour, and textural features of non-touching kernel images. Biosystems Engineering 99(3):330-337.

Douik A, Abdellaoui M (2010). Cereal grain classification by optimal features and intelligent classifiers. International Journal of Computers Communications and Control 5(4):506-516.

Dubey BP, Bhagwat SG, Shouche SP, Sainis JK (2006). Potential of artificial neural networks in varietal identification using morphometry of wheat grains. Biosystems Engineering 95(1):61-67.

FAO (2017). Food and Agriculture Organization of the United Nations Agricultural Statistics. Retrieved 2019 December 4 from http://www.fao.org.

Guevara-Hernandez F, Gil JG (2011). A machine vision system for classification of wheat and barley grain kernels. Spanish Journal of Agricultural Research 3:672-680.

Guzman JD, Peralta EK (2008). Classification of Philippine rice grains using machine vision and artificial neural networks. World Conference on Agricultural Information and IT 6:41-48.

Hamleci B, Güner M (2015). Kestanenin Sıkıştırma Yükü Altındaki Mekanik Davranışlarının Belirlenmesi [Determination of mechanizable response of Chestnuts (Castanea) under compressive loading]. Tarım Makinaları Bilimi Dergisi 11(4):301-307.

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

Kalogirou SA (2001). Artificial neural networks in the renewable energy systems applications: A review. Renewable and Sustainable Energy Reviews 5(4):373-401.

Khoshroo AL, Arefi AR, Masoumiasl AS, Jowkar GH (2014). Classification of wheat cultivars using image processing and artificial neural networks. Agricultural Communications 2:17-22.

Lang P, Dane F, Kubisiak TL (2006). Phylogeny of Castanea (Fagaceae) based on chloroplast trnT-LF sequence data. Tree Genetics & Genomes 2(3):132-139.

Levenberg KA (1944). Method for the solution of certain nonlinear problems in least squares. Quarterly of Applied Mathematics 2:164-168.

Lippman RP (1987). An introduction to computing with neural nets. IEEE ASSP Magazine 4:4-22.

Liu ZY, Cheng F, Ying YB, Rao XQ (2005). Identification of rice seed varieties using neural network. Journal of Zhejiang University. Science B 6(11):1095-1100.

Majumdar S, Jayas DS (2000). Classification of cereal grains using machine vision: IV. Combined morphology, color, and texture models. Transactions of the ASAE 43(6):1689-1694.

Mancuso S, Ferrini F, Nicese FP (1999). Chestnut (Castanea sativa Mill.) genotype identification: An artificial neural network approach. The Journal of Horticultural Science and Biotechnology 74(6):777-784.

Mancuso S, Pisani PL, Bandinelli R, Rinaldelli E (1998). Application of an artificial neural network (ANN) for the identification of grapevine genotypes. Vitis-Geilweilerhof 37:27-32.

Marini F, Bucci R, Magrì AL, Magrì AD, Acquistucci R, Francisci R (2008). Classification of 6 durum wheat cultivars from Sicily (Italy) using artificial neural networks. Chemometrics and Intelligent Laboratory Systems 90(1): 1-7.

Marquardt DW (1963). An Algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics 11:431-441.

Mebatsion HK, Paliwal J, Jayas DS (2013). Automatic classification of non-touching cereal grains in digital images using limited morphological and color features. Computers and Electronics in Agriculture 90:99-105.

Minai AA, Williams RD (1990). Back-propagation heuristics: a study of the extended delta-bar-delta algorithm. International Joint Conference on Neural Networks, San Diego, CA, USA 1:595-600.

Mohsenin NN (1980). Physical properties of plant and animal material. 3rd ed. Gordon and Breach Science Publishers Inc., New York, USA.

Nasirahmadi A, Behroozi-Khazaei N (2013). Identification of bean varieties according to color features using artificial neural network. Spanish Journal of Agricultural Research 11(3):670-677.

Paliwal J, Visen NS, Jayas DS, White ND (2003). Comparison of a neural network and a non-parametric classifier for grain kernel identification. Biosystems Engineering 85(4):405-413.

Pazoki A, Pazoki Z (2011). Classification system for rain fed wheat grain cultivars using artificial neural network. African Journal of Biotechnology 10(41):8031-8038.

Pazoki AR, Farokhi F, Pazoki Z (2014). Classification of rice grain varieties using two artificial neural networks (MLP and Neuro-Fuzzy). The Journal of Animal and Plant Sciences 24(1):336-343.

Pearson T (2010). High-speed sorting of grains by color and surface texture. Applied Engineering in Agriculture 26(3):499-505.

Purushothaman S, Srinivasa YG (1994). A back-propagation algorithm applied to tool wear monitoring. International Journal of Machine Tools and Manufacture 34(5):625-631.

Silva CS, Sonnadara U (2013). Classification of rice grains using neural networks. Institute of Physics - Sri Lanka, Proceedings of Technical Sessions 29:9-14.

Soylu A (2004). Kestane yetiştiriciliği ve özellikleri [Chestnut cultivation and characteristics]. Hasad Yayıncılık, Turkey.

Taner A, Öztekin YB, Tekgüler A, Sauk H, Duran H (2018). Classification of varieties of grain species by artificial neural networks. Agronomy 8(7):123.

Taner A, Tekgüler A, Sauk H (2015). Yapay sinir ağları ile makarnalık buğday çeşitlerinin sınıflandırılması [Classification of durum wheat varieties by artificial neural networks]. Anadolu Tarım Bilimleri Dergisi 30(1):51-59.

TTSM (2019). Registered fruit cultivars. Retrieved 2019 October 1 from https://www.tarimorman.gov.tr/BUGEM/TTSM/Sayfalar/Detay.aspx?SayfaId=85.

Visen NS, Paliwal J, Jayas DS, White ND (2002). Specialist neural networks for cereal grain classification. Biosystems Engineering 82(2):151-159.

Yurtlu YB, Yeşiloğlu E (2011). Mechanical behaviour and split resistance of chestnut under compressive loading. Journal of Agricultural Science 17(4):337-346.

Yurtlu YB, Yeşiloğlu E, Arslanoğlu F (2010). Physical properties of bay laurel seeds. International Agrophysics 24:325-328.

Yurtsever N (1984). Deneysel İstatistik Metotları [Experimental statistical methods]. TCTKB Köy Hizmetleri Genel Müdürlüğü Yayınları No: 121, Ankara.

Zapotoczny P (2011). Discrimination of wheat grain varieties using image analysis and neural networks. Part I. Single kernel texture. Journal of Cereal Science 54(1): 60-68.

Zapotoczny P (2012). Application of image texture analysis for varietal classification of barley. International Agrophysics 26(1):81-90.

Zhang Y, Wang S, Ji G, Phillips P (2014). Fruit classification using computer vision and feedforward neural network. Journal of Food Engineering 143:167-177.

Zhang Y, Wu L (2012). Classification of fruits using computer vision and a multiclass support vector machine. Sensors 12(9):12489-12505.

Published
2020-03-31
How to Cite
ÖZTEKİN, Y. B., TANER, A., & DURAN, H. (2020). Chestnut (Castanea sativa Mill.) cultivar classification: an artificial neural network approach . Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 48(1), 366-377. https://doi.org/10.15835/nbha48111752
Section
Research Articles