• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>2017 56th FITCE Congress >A new method based on computer vision for non-intrusive orange peel sorting
【24h】

A new method based on computer vision for non-intrusive orange peel sorting

机译：基于计算机视觉的非侵入式橙皮分选新方法

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

As it is well-known, orange peel is used for making jam and oil. For this purpose, orange samples with high peel thickness are best. In order to predict peel thickness in orange fruit, we present a system based in image features, comprising: area, eccentricity, perimeter, length/area, blue value, green value, red value, wide, contrast, texture, wide/area, wide/length, roughness, and length. A novel identification solution based on the hybrid of particle swarm optimization (PSO), genetic algorithm (GA) and artificial neural network (ANN) is proposed. In addition, principal component analysis (PCA) has been applied to reduce the number of dimensions, without much loss of information. Taguchi's robust optimization technique has been applied to determine the optimal setting for parameters of PSO, GA, and ANN. The optimal level of factors were: Number of Neuron in first layer=7, Number of Neuron in second layer=2, Maximum Iteration=400, Crossover probability=0.7, Mutation probability=0.1, and Swarm (Population) Size=200. Results for prediction of orange peel thickness based on levels that are achieved by Taguchi method were evaluated by five performance measures: the coefficient of determination (R2), mean squared error (MSE), mean absolute error (MAE), sum square error (SSE), and root mean square error (RMSE), reaching values of 0.8571, 0.0123, 0.0924, 1.392, and 0.1109, respectively.
机译：众所周知，橘子皮用于制造果酱和油。为此，最好使用果皮厚度较高的橙色样品。为了预测橙果的果皮厚度，我们提出了一种基于图像特征的系统，该系统包括：面积，偏心率，周长，长度/面积，蓝色值，绿色值，红色值，宽，对比度，纹理，宽/面积，宽/长，粗糙度和长度。提出了一种基于粒子群算法（PSO），遗传算法（GA）和人工神经网络（ANN）的混合辨识方法。此外，已应用主成分分析（PCA）来减少维数，而不会造成太多信息丢失。 Taguchi的鲁棒优化技术已应用于确定PSO，GA和ANN参数的最佳设置。最佳因素水平为：第一层神经元数量= 7，第二层神经元数量= 2，最大迭代= 400，交叉概率= 0.7，突变概率= 0.1，并且群体（种群）大小= 200。通过Taguchi方法获得的基于水平的橙皮厚度预测结果的评估通过五项性能指标进行评估：测定系数（R 2 ），均方误差（MSE），平均绝对误差（ MAE），平方和误差（SSE）和均方根误差（RMSE），分别达到0.8571、0.0123、0.0924、1.392和0.1109的值。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号