Development of machine vision based system for classification of Guava fruits on the basis of CIE1931 chromaticity coordinates

A. Kanade, A. Shaligram
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引用次数: 6

Abstract

The present work represents a non contact, machine vision based method to estimate ripeness level of Guava fruit. The fruit under test is classified as green, ripe, overripe and spoiled using a web camera based computer vision system. This simple method uses a combination of digital web camera; computer and indigenously developed GUI based software to measure and analyze the surface color of the fruits. The images of the fruit under test are grabbed and displayed on computer screen. Quantitative information such as RGB color distribution, CIE1931 standard based tristimulus values, Chromaticity coordinates and averages (in terms of L_, a_ and b_ values) are computed. The developed software appropriately analyzes the color of the fruit skin and is also capable of classifying the ripening stage of the guava fruit as green, ripe, overripe and spoiled using Principal Component Analysis (PCA). Distinct clusters for the ripeness classes were readily observed in the PCA scatter plot. An Artificial Neural Network (ANN) was also used for a better prediction for unknown samples.
基于CIE1931色度坐标的番石榴分类机器视觉系统的开发
本文提出了一种非接触式、基于机器视觉的番石榴果实成熟度评估方法。使用基于网络摄像头的计算机视觉系统,将被测试的水果分为绿色、成熟、过熟和变质。这种简单的方法结合使用数码网络摄像头;计算机和自主开发的基于GUI的软件来测量和分析水果的表面颜色。被测水果的图像被抓取并显示在电脑屏幕上。计算定量信息,如RGB颜色分布、基于CIE1931标准的三刺激值、色度坐标和平均值(以L_、a_和b_值表示)。所开发的软件对番石榴果皮的颜色进行了适当的分析,并能够使用主成分分析(PCA)将番石榴果实的成熟阶段分为青、熟、过熟和变质。在PCA散点图中很容易观察到不同成熟度的聚类。人工神经网络(ANN)也被用于对未知样本进行更好的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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