Machine vision system for automatic quality grading of fruit
Machine vision system for automatic quality grading of fruit
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DOI：
10.1016/s1537-5110(03)00088-6
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发表时间：
2003-08-01
影响因子：
5.1
通讯作者：
Moltó, E
中科院分区：
文献类型：
--
作者：
Blasco, J;Aleixos, N;Moltó, E
水果和蔬菜通常成批地呈现给消费者。它们的同质性和外观对消费者的决策有重大影响。因此，农产品的呈现从田间到最终消费者的各个阶段都受到操控，并且通常朝着产品清洁和按同质类别分类的方向进行。欧洲信息技术研究与发展战略计划（ESPRIT）第三阶段，编号9230的“水果和蔬菜处理、检验和包装集成系统（SHIVA）”开发了一种用于水果自动、无损检验和处理的机器人系统。本文的目的是报告巴伦西亚农业研究所开发的用于橙子、桃子和苹果质量在线评估的机器视觉技术，并评估这些技术在以下质量属性方面的效率：大小、颜色、果梗位置以及外部瑕疵检测。所使用的基于贝叶斯判别分析的分割程序能够将水果与背景精确区分开来。因此，大小的确定得到了妥善解决。系统所估计的水果颜色与当前用作标准的比色指数值有很好的相关性。在果梗位置确定和瑕疵检测方面也取得了良好的结果。该分类系统对苹果进行了在线测试，在对苹果成批分类时表现良好，瑕疵检测和大小估计的重复性分别为86%和93%。发现该系统的精度和重复性与人工分级的相似。（C）2003年西尔索研究所有限公司。版权所有。由爱思唯尔科学有限公司出版。
Fruit and vegetables are normally presented to consumers in batches. The homogeneity and appearance of these have significant effect on consumer decision. For this reason, the presentation of agricultural produce is manipulated at various stages from the field to the final consumer and is generally oriented towards the cleaning of the product and sorting by homogeneous categories. The project ESPRIT 3, reference 9230 'Integrated system for handling, inspection and packing of fruit and vegetable (SHIVA)' developed a robotic system for the automatic, non-destructive inspection and handling of fruit. The aim of this paper is to report on the machine vision techniques developed at the Instituto Valenciano de Investigaciones Agrarias for the on-line estimation of the quality of oranges, peaches and apples, and to evaluate the efficiency of these techniques regarding the following quality attributes: size, colour, stem location and detection of external blemishes. The segmentation procedure used, based on a Bayesian discriminant analysis, allowed fruits to be precisely distinguished from the background. Thus, determination of size was properly solved. The colours of the fruits estimated by the system were well correlated with the colorimetric index values that are currently used as standards. Good results were obtained in the location of the stem and the detection of blemishes. The classification system was tested on-line with apples obtaining a good performance when classifying the fruit in batches, and a repeatability in blemish detection and size estimation of 86 and 93% respectively. The precision and repeatability of the system, was found to be similar to those of manual grading. (C) 2003 Silsoe Research Institute. All rights reserved. Published by Elsevier Science Ltd.