Predictions of apple bruise volume using artificial neural network

Authors:
Saeed Zarifneshat;Abbas Rohani;Hamid Reza Ghassemzadeh;Morteza Sadeghi;Ebrahim Ahmadi;Masoud Zarifneshat
Affiliations:
Khorasan Razavi Agriculture and Natural Resources Research Center, Mashhad, Iran;Department of Farm Machinery Engineering, College of Agriculture, Shahrood University of Technology, Shahrood, Iran;Department of Agricultural Machinery Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran;Department of Mechanical Engineering, University of Tabriz, Tabriz, Iran;Department of Agricultural Machinery Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran;Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
Venue:
Computers and Electronics in Agriculture
Year:
2012

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Abstract

Bruise damage is a major cause of fruit quality loss. Bruises occur under dynamic and static loading when stress induced in the fruit exceeds the failure stress of the fruit tissue. In this article the potential of an artificial neural network (ANN) technique has evaluated as an alternative method for the prediction of apple bruise volume. Neural bruise estimation models were constructed to calculate Golden Delicious apple bruise volume with respect to fruit properties. The neural models were built based upon impact force and impact energy as the main input parameters including fruit curvature radius, temperature and acoustical stiffness. Optimal parameters for the network were selected via a trial and error procedure on the available data. In this paper, the performance of Basic Backpropagation (BB) training algorithm was also compared with Backpropagation with Declining Learning Rate Factor algorithm (BDLRF). It was found that BDLRF has a better performance for the prediction of apple bruise volume. It is concluded that ANN represents a promising tool for predicting apple bruise volume in comparison to regression model.