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

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...

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Bibliographic Details
Published in:Erwerbsobstbau 2018-06, Vol.60 (2), p.153-160
Main Authors: Eski, İkbal, Demir, Bünyamin, Gürbüz, Feyza, Abidin Kuş, Zeynel, Uğurtan Yilmaz, Kadir, Uzun, Mehmet, Ercişli, Sezai
Format: Article
Language:English
Subjects:
Adaptive systems
Agriculture
Artificial neural networks
Aspect ratio
Biomedical and Life Sciences
Fuzzy systems
Life Sciences
Neural networks
Original Article
Parameter estimation
Physical properties
Plant Sciences
Predictions
Root-mean-square errors
Shape
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Summary: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.
ISSN:0014-0309
1439-0302
DOI:10.1007/s10341-017-0349-3