Building a rule-based machine-vision system for defect inspection on apple sorting and packing lines

被引：69

作者：

Wen, ZQ ^{[1
]}

Tao, Y ^{[1
]}

机构：

[1] Univ Arkansas, Dept Biol & Agr Engn, Fayetteville, AR 72701 USA

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 1999年 / 16卷 / 03期

关键词：

machine vision; infrared; imaging; defect; stem-end; Calyx; inspection; fruit;

D O I：

10.1016/S0957-4174(98)00079-7

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A near-infrared machine-vision system was developed for automating apple defect inspection. Fast blob extraction from fruit images was performed by using an adaptive spherical transformation A binary decision-tree-structured rule base was established using blob feature extraction and analysis. Both off-line and on-line test results demonstrated that the rule-based system was effective for apple defect detection. Compared with the neural network method, the rules-based approach had more flexibility for changing or adding parameters, features and rules to meet various sorting requirements. The technique presented in this paper is being commercialized by a leading manufacturer of fruit/vegetable packinghouse equipment. (C) 1999 Elsevier Science Ltd. All rights reserved.

引用

页码：307 / 313

页数：7