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首页> 外文期刊>International Journal of Natural and Engineering SciencesbElectronic resource >Egg Weight Estimation by Machine Vision and Neural Network Techniques (A case study Fresh Egg)
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Egg Weight Estimation by Machine Vision and Neural Network Techniques (A case study Fresh Egg)

机译：通过机器视觉和神经网络技术估算鸡蛋重量（以新鲜鸡蛋为例）

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Egg weight measurement is one of the most important parameters in marketing this product. Information regarding egg weight is not only vital for grading systems based merely on weight, but it is also necessary for assessing quality indices such as yolk-albumen ratio, shell thickness and hatchability. In the present study a machine vision system combined with artificial neural network technique was used for estimating egg weight. The system hardware consists of a CCD camera, a capture video, an illumination system and a mirror. As an egg is introduced into the frame, grabber two perpendicular images are grabbed. These images are then processed in MATLAB and pixel data corresponding to each image edge is extracted. Once center of gravity of each image edge is obtained, twelve size features can be calculated for each image. These features are then classified into three categories named as input vectors (1-3). Each input vector along with its real weight data (measured) is exported to three parallel training algorithms of a Multi Layer Perceptron (MLP) Network. The training algorithms are variable learning rate (MLP-GDX), resilient back propagation (MLP-RP) and scaled conjugate gradient (MLP-SCG). These training algorithms were optimized to estimate egg weight. Evaluation results showed that MLP-SCG was superior to other two algorithms in estimating egg weight at high accuracy (R=.96). In other words, MLP-SCG was capable of egg weight estimation at an absolute error of no more than 2.3g for the average egg size of 60 g.
机译：鸡蛋重量测量是该产品销售中最重要的参数之一。有关蛋重的信息不仅对于仅基于重量的分级系统至关重要，而且对于评估蛋黄/蛋清比率，蛋壳厚度和孵化率等质量指标也是必不可少的。在本研究中，结合了人工神经网络技术的机器视觉系统用于估计蛋重。系统硬件包括CCD摄像机，捕获视频，照明系统和镜子。将鸡蛋放入框架中时，抓取器会抓取两个垂直的图像。然后在MATLAB中处理这些图像，并提取与每个图像边缘相对应的像素数据。一旦获得每个图像边缘的重心，就可以为每个图像计算十二个尺寸特征。然后将这些特征分为三类，分别称为输入向量（1-3）。每个输入向量及其实际重量数据（测量值）都将导出到多层感知器（MLP）网络的三种并行训练算法中。训练算法是可变学习率（MLP-GDX），弹性反向传播（MLP-RP）和比例共轭梯度（MLP-SCG）。这些训练算法被优化以估计蛋重。评估结果表明，MLP-SCG在高精度估计蛋重方面优于其他两种算法（R = .96）。换句话说，对于60 g的平均鸡蛋大小，MLP-SCG能够以不超过2.3 g的绝对误差估算鸡蛋重量。

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