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Fresh egg mass estimation using machine vision technique

机译:使用机器视觉技术估算新鲜鸡蛋质量

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摘要

In the present study a machine vision system was developed for estimating the mass of eggs arranged in a single array. A grabber frame equipped with a mirror was developed for positioning the eggs. Therefore, two images could be captured from each egg. Images were then processed by Matlab software. Six algorithms were developed to extract eggs features such as minimum, maximum and effective radii, perimeter and the frontal area from each image. The eggs were also weighed by a sensitive digital scale. Seventy percent of data after discarding the outliers were used to establish some models, and the remaining was used to verify the final model. The results showed that egg mass estimation can be accurate by using two perpendicular views of each egg. Amongst the models, one with predictors of area and effective radius was found to be the best. A high correlation coefficient was observed between eggs mass measured and predicted by the model, with an accuracy of about 95%.
机译:在本研究中,开发了一种机器视觉系统,用于估计排列成单个阵列的鸡蛋的质量。开发了配有镜子的抓斗框架,用于放置卵。因此,可以从每个鸡蛋中捕获两个图像。然后通过Matlab软件处理图像。开发了六种算法来从每个图像中提取卵的特征,例如最小,最大和有效半径,周长和额叶面积。鸡蛋也用灵敏的数字秤称重。丢弃离群值后,有70%的数据用于建立一些模型,其余的用于验证最终模型。结果表明,通过使用每个鸡蛋的两个垂直视图,可以准确估算鸡蛋质量。在这些模型中,发现具有面积和有效半径的预测因子的模型是最好的。在模型测量和预测的鸡蛋质量之间观察到较高的相关系数,准确度约为95%。

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