Predictions of apple bruise volume using artificial neural network
Predictions of apple bruise volume using artificial neural network
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DOI：
10.1016/j.compag.2011.12.015
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发表时间：
2012-03-01
影响因子：
8.3
通讯作者：
Zarifneshat, Masoud
中科院分区：
文献类型：
--
作者：
Zarifneshat, Saeed;Rohani, Abbas;Zarifneshat, Masoud
碰伤是造成果实品质损失的主要原因之一。在动态和静态载荷下，当果实中引起的应力超过果实组织的破坏应力时，就会发生压伤。在这篇文章中的人工神经网络（ANN）技术的潜力进行了评估，作为一种替代方法预测苹果碰伤量。建立了金冠苹果损伤量的神经网络损伤估计模型。以冲击力和冲击能量为主要输入参数，包括果实曲率半径、温度和声学刚度，建立了神经网络模型。网络的最佳参数是通过对现有数据的试错程序选择的。在本文中，基本反向传播（BB）训练算法的性能进行了比较与反向传播与下降学习率因子算法（BDLRF）。研究发现，BDLRF对于苹果瘀伤体积的预测具有更好的性能。它的结论是，人工神经网络是一个很有前途的工具，预测苹果碰伤体积相比，回归模型。(C)2011爱思唯尔有限公司版权所有。
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. (C) 2011 Elsevier B.V. All rights reserved.