Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning
Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning
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DOI：
10.1049/iet-ipr.2019.0738
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发表时间：
2020-09-18
影响因子：
2.3
通讯作者：
Grout, Vic
中科院分区：
文献类型：
--
作者：
Alam Siddiquee, Kazy Noor e;Islam, Md. Shabiul;Grout, Vic
在这项研究中，专门针对作物地里有/没有缺陷的成熟/未成熟番茄的检测，描述了两种不同的方法，并对安装在移动机器人上的摄像头捕获的图像进行了比较。一种是机器学习方法，被称为级联对象检测器(COD)，另一种是传统定制方法的组合，分别被称为“颜色变换”：“颜色分割”和“圆形霍夫变换”。(Viola-Jones)COD生成了“定向梯度直方图”(HOG)特征来检测西红柿。对于成熟度检查，RGB平均值是根据一组规则计算的。然而，对于传统的方法，应用颜色阈值来检测自然或固体背景中的番茄，并调整RGB颜色来识别成熟的番茄。在最优和平均情况下，该算法对于任何基于微控制器的微电子设备都是最优可行的，其运行时间复杂度为传统方法的Ofo(n(3))。比较结果表明，机器学习方法的准确率为95%，优于基于MatLab的颜色分割方法。
In this study, specifically for the detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared from captured images by a camera mounted on a mobile robot. One is a machine learning approach, known as 'Cascaded Object Detector' (COD) and the other is a composition of traditional customised methods, individually known as 'Colour Transformation': 'Colour Segmentation' and 'Circular Hough Transformation'. The (Viola-Jones) COD generates 'histogram of oriented gradient' (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, colour thresholding is applied to detect tomatoes either from natural or solid background and RGB colour is adjusted to identify ripened tomatoes. This algorithm is shown to be optimally feasible for any micro-controller based miniature electronic devices in terms of its run time complexity ofO(n(3)) for a traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB.