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首页> 美国卫生研究院文献>Sensors (Basel, Switzerland) >In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging
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In-Line Sorting of Harumanis Mango Based on External Quality Using Visible Imaging

机译：基于外观质量基于可视化的Harumanis芒果的在线分选

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The conventional method of grading Harumanis mango is time-consuming, costly and affected by human bias. In this research, an in-line system was developed to classify Harumanis mango using computer vision. The system was able to identify the irregularity of mango shape and its estimated mass. A group of images of mangoes of different size and shape was used as database set. Some important features such as length, height, centroid and parameter were extracted from each image. Fourier descriptor and size-shape parameters were used to describe the mango shape while the disk method was used to estimate the mass of the mango. Four features have been selected by stepwise discriminant analysis which was effective in sorting regular and misshapen mango. The volume from water displacement method was compared with the volume estimated by image processing using paired t-test and Bland-Altman method. The result between both measurements was not significantly different (P > 0.05). The average correct classification for shape classification was 98% for a training set composed of 180 mangoes. The data was validated with another testing set consist of 140 mangoes which have the success rate of 92%. The same set was used for evaluating the performance of mass estimation. The average success rate of the classification for grading based on its mass was 94%. The results indicate that the in-line sorting system using machine vision has a great potential in automatic fruit sorting according to its shape and mass.
机译：评级Harumanis芒果的常规方法耗时，昂贵且受人为偏见的影响。在这项研究中，开发了一种在线系统以使用计算机视觉对Harumanis芒果进行分类。该系统能够识别出芒果形状的不规则性及其估计的质量。将一组不同大小和形状的芒果图像用作数据库集。从每个图像中提取了一些重要特征，例如长度，高度，质心和参数。傅里叶描述子和大小形状参数用于描述芒果的形状，而圆盘法用于估计芒果的质量。通过逐步判别分析选择了四个特征，这些特征可以有效地对常规芒果和畸形芒果进行分类。将水置换法的体积与通过配对t检验和Bland-Altman方法通过图像处理估算的体积进行比较。两次测量之间的结果无显着差异（P> 0.05）。对于由180个芒果组成的训练集，形状分类的平均正确分类率为98％。数据通过另一组包含140个芒果的测试集进行了验证，其成功率为92％。同一组用于评估质量估计的性能。基于质量的分级分类的平均成功率为94％。结果表明，基于机器视觉的在线分拣系统根据其形状和质量在自动分拣水果方面具有很大的潜力。

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