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首页> 外文期刊>Spanish Journal of Agricultural Research >An automatic and non-intrusive hybrid computer vision system for the estimation of peel thickness in Thomson orange
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An automatic and non-intrusive hybrid computer vision system for the estimation of peel thickness in Thomson orange

机译：自动和非侵入式混合计算机视觉系统，用于估算汤姆森橙皮的厚度

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Orange peel has important flavor and nutrition properties and is often used for making jam and oil in the food industry. For previous reasons, oranges with high peel thickness are valuable. In order to properly estimate peel thickness in Thomson orange fruit, based on a number of relevant image features (area, eccentricity, perimeter, length/area, blue component, green component, red component, width, contrast, texture, width/area, width/length, roughness, and length) a novel automatic and non-intrusive approach based on computer vision with a hybrid particle swarm optimization (PSO), genetic algorithm (GA) and artificial neural network (ANN) system is proposed. Three features (width/area, width/length and length/area ratios) were selected as inputs to the system. A total of 100 oranges were used, performing cross validation with 100 repeated experiments with uniform random samples test sets. Taguchi’s robust optimization technique was applied to determine the optimal set of parameters. Prediction results for orange peel thickness (mm) based on the levels that were achieved by Taguchi’s method were evaluated in several ways, including orange peel thickness true-estimated boxplots for the 100 orange database and various error parameters: the sum square error (SSE), the mean absolute error (MAE), the coefficient of determination ( R 2 ), the root mean square error (RMSE), and the mean square error (MSE), resulting in mean error parameter values of R 2 =0.854±0.052, MSE=0.038±0.010, and MAE=0.159±0.023, over the test set, which to our best knowledge are remarkable numbers for an automatic and non-intrusive approach with potential application to real-time orange peel thickness estimation in the food industry.
机译：桔皮具有重要的风味和营养特性，在食品工业中通常用于制造果酱和油。由于先前的原因，具有高果皮厚度的橘子很有价值。为了根据许多相关的图像特征（面积，偏心率，周长，长度/面积，蓝色分量，绿色分量，红色分量，宽度，对比度，质地，宽度/面积，提出了一种基于计算机视觉，混合粒子群优化（PSO），遗传算法（GA）和人工神经网络（ANN）系统的基于计算机视觉的自动非侵入式新方法。选择了三个要素（宽度/面积，宽度/长度和长度/面积比）作为系统的输入。总共使用了100个橙子，使用统一的随机样本测试集对100个重复实验进行交叉验证。 Taguchi的鲁棒优化技术用于确定最佳参数集。基于Taguchi方法获得的水平的橘皮厚度（mm）的预测结果以多种方式进行了评估，包括针对100个橘子数据库的橘皮厚度真实估计的箱线图和各种误差参数：和平方误差（SSE） ，平均绝对误差（MAE），确定系数（R 2），均方根误差（RMSE）和均方误差（MSE），从而得出R 2 = 0.854±0.052的平均误差参数值，在测试集上，MSE = 0.038±0.010，MAE = 0.159±0.023，据我们所知，这是一种自动和非侵入式方法的显着数字，有可能应用于食品工业中实时橙皮厚度估计。

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