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Design of Neural Network Predictor for the Physical Properties of Almond Nuts

Design des neuronalen Netzes als Prädiktor für die physikalischen Eigenschaften von Mandeln

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Abstract

In this study, an adaptive neuro fuzzy interface system (ANFIS) based predictor was designed to predict the physical properties of four almond types. Measurements of the dimensions, length, width and thickness were carried out for one hundred randomly selected samples of each type. With using these three major perpendicular dimensions, some physical parameters such as projected area, arithmetic mean diameter, geometric mean diameter, sphericity, surface area, volume, shape index and aspect ratio were estimated. In in a various Artificial Neural Network (ANN) structures, ANFIS structure which has given the best results was selected. The parameters analytically estimated and those predicted were given in the form of figures. The root mean-squared error (RMSE) was found to be 0.0001 which is quite low. ANFIS approach has given a superior outcome in the prediction of the Physical Properties of Almond Nuts.

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References

  • Afonso Junior PC, Correa PC, Pinto FAC, Queiroz DM (2007) Aerodynamic properties of coffee cherries and beans. Biosyst Eng 98:39–46

    Article  Google Scholar 

  • Aktas T, Polat R, Atay U (2007) Comparison of mechanical properties of some selected almond cultivars with hard and soft shell under compression loading. J Food Process Eng 30:773–789

    Article  Google Scholar 

  • Arslan S, Vursavus KK (2008) Physico-mechanical properties of almond nut and its kernel as a function of variety and moisture content. Philipp Agric Sci 91:171–179

    Google Scholar 

  • Ayoubi S, Shahri AP, Karchegani PM, Sahrawat KL (2011) Application of Artificial Neural Network (ANN) to predict soil organic matter using remote sensing data in two ecosystems. In: Atazadeh I (ed) Biomass and remote sensing of biomass, pp 181–196

    Google Scholar 

  • Bachir O, Zoubir AF (2012) Adaptive Neuro-fuzzy inference system based control of Puma 600 robot manipulator. Int J Electr Comput Eng 2:90–97

    Google Scholar 

  • Bala BK, Ashraf MA, Uddin MA, Janjai S (2005) Experimental and neural network prediction of the performance of a solar tunnel drier for drying jackfruit bulbs and leather. J Food Process Eng 28:552–566

    Article  Google Scholar 

  • Diamantopoulou MJ (2005) Artificial neural networks as an alternative tool in pine bark volume estimation. Comput Electron Agric 48:235–244

    Article  Google Scholar 

  • Dousti A, Ghazavi MA, Maleki A (2013) Grading of empty walnut using signal processing and artificial neural network techniques. Int J Agric Crop Sci 6:1072

    Google Scholar 

  • FAO (2012) FAO web page. www.fao.org. Accessed 10.11.2016

    Google Scholar 

  • Goyal S (2013) Artificial neural networks in vegetables: a comprehensive review. Sci J Crop Sci 2:75–94

    Google Scholar 

  • Karimi H, Navid H, Mahmoudi A (2012) Detection of damaged seeds in laboratory evaluation of precision planter using impact acoustics and artificial neural networks. J Artif Intell Res 1:67

    Google Scholar 

  • Kester DE, Gradziel TM (1996) Almonds. Fruit breeding. In: Janick J, Moore JN (eds) vol III. Wiley & Sons, Hoboken, pp 1–240. ISBN 978-0471126690

    Google Scholar 

  • Khalesi S, Mahmoudi A, Hosainpour A, Alipour A (2012) Detection of walnut varieties using impact acoustics and artificial neural networks (ANNs). Mod Appl Sci 6:43

    Google Scholar 

  • Khalifa S, Komarizadeh MH, Tousi B, Nikbakht AM (2011) An intelligent system for grading walnuts based on acoustic emission and neural networks. J Food Agric Environ 9:109–112

    Google Scholar 

  • Konečný V, Trenz O, Svobodová E (2010) Classification of companies with the assistance of self-learning neural networks. Agric Econ 56:51–58

    Google Scholar 

  • Mahmood M, Mojtaba T, Javad K, Narjes M (2008) Modeling some mechanical properties distributions of almond using weibull function. 18th National Congress on Food Technology, Iran, pp 1–6

    Google Scholar 

  • Mahmoodi-Eshkaftaki M, Ebrahimi R, Torki-Harchegani M (2013) Determination of critical conditions for puncturing almonds using coupled response surface methodology and genetic algorithm. Food Technol Biotechnol 51:500–508

    Google Scholar 

  • Mancuso S, Ferrini F, Nicese FP (1999) Chestnut (Castanea sativa mill.) genotype identification: an artificial neural network approach. J Hortic Sci Biotechnol 74:777–784

    Article  Google Scholar 

  • McCabe WL, Smith JC, Harriot P (1986) Unit operations of chemical engineering. McGraw-Hill, New York

    Google Scholar 

  • Mirzabe AH, Khazaei J, Chegini GR, Gholami O (2013) Some physical properties of almond nut and kernel and modeling dimensional properties. Agric Eng Int 15:256–265

    Google Scholar 

  • Mohsenin NN (1986) Physical properties of plant and animal materials. Gordon & Breach Science, New York

    Google Scholar 

  • Movagharnejad K, Nikzad M (2007) Modeling of tomato drying using artificial neural network. Comput Electron Agric 59:78–85

    Article  Google Scholar 

  • Mpotokwane SM, Gaditlhatlhelwe E, Sebaka A, Jideani VA (2008) Physical properties of bambara groundnuts from Botswana. J Food Eng 89:93–98

    Article  Google Scholar 

  • Reshadsedghi A, Mahmoudi A (2013) Detection of almond varieties using impact acoustics and artificial neural networks. Int J Agric Crop Sci 6:1008

    Google Scholar 

  • Reshadsedghi A, Mahmoudi A, Azimirad V, Hajilou J, Ghaffari H (2014) Non-destructive detection of unshelled almonds quality based on their kernel percentage using impact-acoustics and ANN’s techniques. Agric Sci Dev 3:360–365

    Google Scholar 

  • Rugini E, Monastra F (2003) Temperate fruits. In: Mitra SK, Rathora DS, Bose TK (eds) vol II. Display Printers, India, pp 344–414. ISBN 978-8190017114

    Google Scholar 

  • Sayinci B, Ercişli S, Akbulut M, Şavşatli Y, Baykal H (2015) Determination of shape in fruits of cherry laurel (Prunus laurocerasus) accessions by usıng elliptic fourier analysis. Acta Sci Pol Hortorum Cultus 14:63–82

    Google Scholar 

  • Soylu A (2003) Ilıman iklim Meyveleri II. Ders Notları No: 72. Uludağ Üniversitesi Zir. Fak., Bursa, pp 204–220

    Google Scholar 

  • Svoboda E (2007) Knowledge-management in managerial work of business management. Agric Econ 53:298–303

    Google Scholar 

  • Šťastný J, Konečný V, Trenz O (2011) Agricultural data prediction by means of neural network. Agric Econ 57:356–361

    Google Scholar 

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Authors and Affiliations

  1. Department of Mechatronics Engineering, Faculty of Engineering, University of Erciyes, 38039, Kayseri, Turkey

    İkbal Eski

  2. Department of Mechanical and Metal Technologies, Vocational School of Technical Sciences, University of Mersin, 33343, Mersin, Turkey

    Bünyamin Demir

  3. Department of Industrial Engineering, Faculty of Engineering, University of Erciyes, 38039, Kayseri, Turkey

    Feyza Gürbüz

  4. Department of Biosystems Engineering, Faculty of Agriculture, University of Erciyes, 38039, Kayseri, Turkey

    Zeynel Abidin Kuş

  5. Department of Horticulture, Faculty of Agriculture, University of Erciyes, 38039, Kayseri, Turkey

    Kadir Uğurtan Yilmaz

  6. Pistachio Research Institute, 2706, Gaziantep, Turkey

    Mehmet Uzun

  7. Department of Horticulture, Faculty of Agriculture, University of Atatürk, 25240, Erzurum, Turkey

    Sezai Ercişli

Authors
  1. İkbal Eski
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  2. Bünyamin Demir
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  3. Feyza Gürbüz
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  4. Zeynel Abidin Kuş
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  5. Kadir Uğurtan Yilmaz
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  6. Mehmet Uzun
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  7. Sezai Ercişli
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Correspondence to Bünyamin Demir.

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Conflict of interest

B. Demir, İ. Eski, F. Gürbüz, Z. Abidin Kuş, K. Uğurtan Yilmaz, M. Uzun and S. Ercişli declare that they have no competing interests.

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Eski, İ., Demir, B., Gürbüz, F. et al. Design of Neural Network Predictor for the Physical Properties of Almond Nuts. Erwerbs-Obstbau 60, 153–160 (2018). https://doi.org/10.1007/s10341-017-0349-3

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  • DOI: https://doi.org/10.1007/s10341-017-0349-3

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