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首页> 外文期刊>Acta Horticulturae >Mass estimation of mangoes by processing of white background grayscale images.
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Mass estimation of mangoes by processing of white background grayscale images.

机译：通过处理白色背景灰度图像对芒果进行质量估计。

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The postharvest grading of mangoes normally includes mass measurement using expensive dynamic weighing systems to group similar fruits in the same package. Small producers cannot buy systems like these which are normally complex to install and operate/maintain. This work describes a study for grading of mangoes ( Mangifera indica L., 'Tommy Atkins') based on image processing. It aims to apply image processing, afterwards, on a low cost postharvest fruit selection and grading system. A hundred mangoes were acquired at the local market of Joao Pessoa (PB), northeastern of Brazil, and divided into two 50 units groups, randomly. A Sony TM P7 digital camera was fixed at the center top of a closed polystyrene box (50*50*50 cm), internal walls covered with white paper. Two PL(20W) lamps illuminated the mango at the center bottom of the box. Top view picture of the fruit, at rest, was taken. An offline Matlab TM software calculated the top area of the fruits using a new threshold value y=x+x.(m/k), where x: Otsu's threshold; m: mean intensity of the image previously segmented using Otsu's algorithm; k: empirical constant, ambient light dependent. All the fruits were weighed using a digital scale and the first group was used in order to find (projected top area * actual mass) best linear relation: m(g)=-108.7+0.002649.A(pixels 2). The other 50 fruits images were used aiming to validate the equation found. Result showed a highly linear correlation between the top area and the measured masses (R 2=0.96, SD=15.7 g) for the first group. It was possible to estimate, by using the area of the top images, the masses of the mangoes belonging to the second group. The (predicted * actual) mass values showed R 2=0.94, SD=19.8 g. Research is being carried out in order to improve the grading system on a conveyor belt, indicating also the possible use with other horticultural products. CT XXVIII International Horticultural Congress on Science and Horticulture for People (IHC2010): International Symposium on Postharvest Technology in the Global Market, Lisbon, Portugal.
机译：芒果的收获后分级通常包括使用昂贵的动态称重系统进行质量测量，以将相似的水果分组在同一包装中。小型生产商无法购买此类系统，这些系统通常安装和操作/维护都很复杂。这项工作描述了基于图像处理对芒果（Mangifera indica L。，“ Tommy Atkins”）进行分级的研究。之后，它旨在将图像处理应用于低成本的采后水果选择和分级系统。在巴西东北部的Joao Pessoa（PB）的当地市场上收购了一百只芒果，并随机分为两个50个单位的组。将索尼TM P7数码相机固定在一个封闭的聚苯乙烯盒（50 * 50 * 50厘米）的中央顶部，内壁覆盖有白纸。两个PL（20W）灯照亮了盒子中央底部的芒果。水果的顶视图图片，静止不动，已拍摄。离线的Matlab TM软件使用新的阈值y = x + x。（m / k）计算水果的顶部面积，其中x：大津的阈值; m：以前使用Otsu算法分割的图像的平均强度; k：经验常数，取决于环境光。使用数字秤对所有水果进行称重，并使用第一组来找到（投影的顶部面积*实际质量）最佳线性关系：m（g）=-108.7 + 0.002649.A（像素2）。使用其他50个水果图像来验证找到的方程。结果显示，第一组的顶部面积与测量质量之间存在高度线性相关性（R 2 = 0.96，SD = 15.7 g）。通过使用顶部图像的面积，可以估计属于第二组的芒果的质量。 （预测的*实际的）质量值显示R 2 ＝ 0.94，SD ＝ 19.8g。为了改善传送带上的分级系统，正在进行研究，这也表明可能与其他园艺产品一起使用。 CT XXVIII国际人类园艺科学园艺大会（IHC2010）：全球市场收获后技术国际研讨会，葡萄牙里斯本。

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