Using artificial neural network (ANN) technique for prediction of apple bruise damage

Transporting vibration is a major cause of bruising damage in apples. In this study the potential of Artificial Neural Network (ANN) technique to predict bruise volume of apple was evaluated and a prediction model for bruising apple in transport condition (by truck) was developed. For this purpose, firstly significant or non-significant effects of transport vibration parameters (frequency and acceleration) and fruit properties (mass, curvature radius, acoustical stiffness) on bruising of apple in transport condition was investigated and the parameters that have significant effects identified. This parameters were considered as input variables in the develop model. In the second stage, by using of these input variables a multilayer perceptron (MLP) network trained and prediction model was developed. Results indicated that the model with 5-7-1 instruction, sigmoid transfer function in hidden layer and linear transfer function in output layer with 40000 epochs gives the best correlation between predicted and actual values, with correlation coefficient (R2) of 0.9998 and 0.9996 in training and testing phase, respectively. It can predict the bruise volume with acceptable root mean-squared error (RMSE) of 4.21. Also a regression model for prediction with correlation coefficient (R2) of 0.9996 was developed. These results indicated that the Artificial Neural Network technique could potentially be used to predict apple bruising in transport condition.

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