Predictions of apple bruise volume using artificial neural network

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.

► Neural bruise estimation models were constructed to calculate apple bruise volume.
► Models were built based upon impact force and impact energy as main input network.
► BB training algorithm compared with BDLRF algorithm.
► BDLRF has a better performance for the prediction of apple bruise volume.
► An artificial neural network is able to predict apple bruise volume better than a linear regression model.