Evaluation of image processing technique and discriminant analysis methods in postharvest processing of carrot fruit.,Food Science & Nutrition

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Evaluation of image processing technique and discriminant analysis methods in postharvest processing of carrot fruit.
Food Science & Nutrition ( IF 3.5 ) Pub Date : 2020-05-18 , DOI: 10.1002/fsn3.1614
Ahmad Jahanbakhshi ₁ , Kamran Kheiralipour ₂

Affiliation

The most important process before packaging and preserving agricultural products is sorting operation. Sort of carrot by human labor is involved in many problems such as high cost and product waste. Image processing is a modern method, which has different applications in agriculture including classification and sorting. The aim of this study was to classify carrot based on shape using image processing technique. For this, 135 samples with different regular and irregular shapes were selected. After image acquisition and preprocessing, some features such as length, width, breadth, perimeter, elongation, compactness, roundness, area, eccentricity, centroid, centroid nonhomogeneity, and width nonhomogeneity were extracted. After feature selection, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) methods were used to classify the features. The classification accuracies of the methods were 92.59 and 96.30, respectively. It can be stated that image processing is an effective way in improving the traditional carrot sorting techniques.

中文翻译：

胡萝卜果实采后加工中的图像处理技术和判别分析方法评价

农产品包装保鲜前最重要的一道工序是分选作业。人工分拣胡萝卜存在成本高、产品浪费等诸多问题。图像处理是一种现代方法，在农业中具有不同的应用，包括分类和排序。本研究的目的是使用图像处理技术根据形状对胡萝卜进行分类。为此，选择了 135 个不同规则和不规则形状的样品。经过图像采集和预处理，提取了长度、宽度、宽度、周长、伸长率、紧密度、圆度、面积、偏心率、质心、质心不均匀性和宽度不均匀性等特征。特征选择后，使用线性判别分析（LDA）和二次判别分析（QDA）方法对特征进行分类。方法的分类准确率分别为92.59和96.30。可以说，图像处理是改进传统胡萝卜分选技术的有效途径。

更新日期：2020-05-18

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