Prediction of Walnut Mass Based on Physical Attributes by Artificial Neural Network (ANN)
Author:
Publisher
Springer Science and Business Media LLC
Subject
Horticulture
Link
http://link.springer.com/content/pdf/10.1007/s10341-019-00468-8.pdf
Reference36 articles.
1. Afonso Junior PC, Correa PC, Pinto FAC, Queiroz DM (2007) Aerodynamic properties of coffee cherries and beans. Biosystems Engineering 98:39–46
2. Altuntaş E, Erkol M (2010) Physical properties of shelled and kernel walnuts as affected by the moisture content. Czech J Food Sci 28(6):547–556
3. Ashtiani SHM, Motie JB, Emadi B, Aghkhani MH (2014) Models for predicting the mass of lime fruits by some engineering properties. J Food Sci Technol 51(11):3411–3417
4. Demir B, Eski I, Kus ZA, Ercisli S (2017) Prediction of physical parameters of pumpkin seeds using neural network. Notulae Botanicae Horti Agrobotanici Cluj-Napoca 45(1):22–27
5. Eliseeva L, Yurina O, Hovhannisyan N (2017) Nuts as raw material for confectionary industry. Ann Agrar Sci 15(1):71–74
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