北京市农林科学院机构知识库

Machine vision technology for detecting the external defects of fruits - a review

收藏
分享
全文链接

文献类型：外文期刊

作者： Li, J. B. ¹ ; Huang, W. Q. ¹ ; Zhao, C. J. ¹ ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

关键词： Fruits;Machine vision;Colour imaging;Monochrome imaging;Hyperspectral imaging;External defects

期刊名称：IMAGING SCIENCE JOURNAL （影响因子：0.871；五年影响因子：0.987 ）

ISSN： 1368-2199

年卷期： 2015 年 63 卷 5 期

页码：

收录情况： SCI

摘要： Machine vision is a rapid, consistent and objective inspection technique, which has expanded into many diverse industries. Its speed and accuracy provide one alternative for an automated, non-destructive and cost-effective technique to accomplish ever-increasing production and quality requirements. This method of inspection has found applications in the agricultural industry, including the inspection and grading of fruits. The presence of skin defects is one of the most influential factors in the price of fruit, since most consumer associate quality with a good appearance and the total absence of external defects. However, defect is more difficult than colour, shape and size to be inspected and graded. In this paper, current applications of machine vision in fruit external defects detection are briefly reviewed. Some detection technologies were presented. These technologies include image processing methods based on fruit two-dimensional and three-dimensional (3D) information, and hyperspectral and multispectral imaging. In addition, their advantages and disadvantages are also discussed. Development of multi-camera combination imaging systems will ensure that computer vision technique continues to meet the accuracy and quality requirements needed in this highly competitive and challenging fruit industry. However, reducing of processing time and merging analysis of different type of images will be still a challenge since the combination systems are more complicated in a real-time application.

相关文献

[1]Recognition of wheat preharvest sprouting based on hyperspectral imaging. Wu, Qiong,Wang, Jihua,Wu, Qiong,Zhu, Dazhou,Wang, Cheng,Ma, Zhihong,Wang, Jihua. 2012

[2]Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Zhang, Baohua,Huang, Wenqian,Li, Jiangbo,Zhao, Chunjiang,Fan, Shuxiang,Wu, Jitao,Zhang, Baohua,Zhao, Chunjiang,Liu, Chengliang. 2014

[3]Design and Test of Tomatoes Harvesting Robot. Feng, Qingchun,Wang, Xiaonan,Wang, Guohua,Li, Zhen. 2015

[4]Monitoring of Winter Wheat Aboveground Fresh Biomass Based on Multi-Information Fusion Technology. Zheng Ling,Dong Da-ming,Zhang Bao-hua,Wang Cheng,Zhao Chun-jiang,Zheng Ling,Zhu Da-zhou. 2016

[5]Detection of defects on apple using B-spline lighting correction method. Li, Jiangbo,Huang, Wenqian,Guo, Zhiming. 2013

[6]Developing a machine vision system for simultaneous prediction of freshness indicators based on tilapia (Oreochromis niloticus) pupil and gill color during storage at 4 degrees C. Shi, Ce,Qian, Jianping,Han, Shuai,Fan, Beilei,Yang, Xinting,Wu, Xiaoming,Shi, Ce,Qian, Jianping,Han, Shuai,Fan, Beilei,Yang, Xinting,Wu, Xiaoming,Shi, Ce,Qian, Jianping,Han, Shuai,Fan, Beilei,Yang, Xinting,Wu, Xiaoming. 2018

[7]Segmentation of Cotton Leaves Based on Improved Watershed Algorithm. Niu, Chong,Niu, Yuguang,Niu, Chong,Li, Han,Zheng, Wengang,Niu, Chong,Li, Han,Zheng, Wengang,Zhou, Zengchan,Bu, Yunlong. 2016

[8]Design and test of tray-seedling sorting transplanter. Feng Qingchun,Zhao Chunjiang,Jiang Kai,Fan Pengfei,Wang Xiu. 2015

[9]Automatic detection of defective apples using NIR coded structured light and fast lightness correction. Zhang, Chi,Zhao, Chunjiang,Huang, Wenqian,Wang, Qingyan,Liu, Shenggen,Li, Jiangbo,Guo, Zhiming.

[10]Cumulative risk assessment of the exposure to pyrethroids through fruits consumption in China - Based on a 3-year investigation. Li, Zhixia,Nie, Jiyun,Xu, Guofeng,Yan, Zhen,Li, Zhixia,Nie, Jiyun,Xu, Guofeng,Yan, Zhen,Lu, Zeqi,Xie, Hanzhong,Kang, Lu,Chen, Qiusheng,Li, An,Zhao, Xubo.

[11]Cloning, characterization and expression of the gene encoding polygalacturonase-inhibiting proteins (PGIPs) of peach [prunus persica (L.) Batch]. Liang, FS,Zhang, KC,Zhou, CJ,Kong, FN,Li, J,Wang, B.

[12]A single-step solid phase extraction for the simultaneous determination of 8 mycotoxins in fruits by ultra-high performance liquid chromatography tandem mass spectrometry. Wang, Meng,Jiang, Nan,Xian, Hong,Wei, Dizhe,Feng, Xiaoyuan,Wang, Meng,Jiang, Nan,Xian, Hong,Wei, Dizhe,Feng, Xiaoyuan,Shi, Lei.

[13]Geographical classification of apple based on hyperspectral imaging. Guo, Zhiming,Huang, Wenqian,Chen, Liping,Zhao, Chunjiang. 2013

[14]Identification of seedling cabbages and weeds using hyperspectral imaging. Wei, Deng,Zhao Chunjiang,Xiu, Wang,Huang, Yanbo,Wei, Deng,Zhao Chunjiang,Xiu, Wang,Wei, Deng,Zhao Chunjiang,Xiu, Wang,Wei, Deng,Zhao Chunjiang,Xiu, Wang. 2015

[15]Effectively Predicting Soluble Solids Content in Apple Based on Hyperspectral Imaging. Huang Wen-qian,Li Jiang-bo,Chen Li-ping,Guo Zhi-ming. 2013

[16]Comparative analysis of models for robust and accurate evaluation of soluble solids content in 'Pinggu' peaches by hyperspectral imaging. Chen, Liping. 2017

[17]Detection of Wheat Powdery Mildew by Differentiating Background Factors using Hyperspectral Imaging. Zhang, Dongyan,Zhang, Lifu,Zhang, Dongyan,Wang, Xiu,Zhang, Dongyan,Wang, Xiu,Lin, Fenfang,Huang, Yanbo. 2016

[18]Rapid determination of biogenic amines in cooked beef using hyperspectral imaging with sparse representation algorithm. Yang, Dong,Lu, Anxiang,Wang, Jihua,Yang, Dong,Wang, Jihua,Lu, Anxiang,Ren, Dong,Wang, Jihua,Lu, Anxiang,Wang, Jihua. 2017

[19]Measuring the Moisture Content in Maize Kernel Based on Hyperspctral Image of Embryo Region. Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua,Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua,Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua,Tian Xi,Huang Wen-qian,Li Jiang-bo,Fan Shu-xiang,Zhang Bao-hua. 2016

[20]Principles and Applications of Hyperspectral Imaging Technique in Quality and Safety Inspection of Fruits and Vegetables. Zhang Bao-hua,Zhang Bao-hua,Li Jiang-bo,Fan Shu-xiang,Huang Wen-qian,Zhang Chi,Wang Qing-yan,Xiao Guang-dong. 2014

作者其他论文更多>>

CARBON FOOTPRINT ASSESSMENT OF A LARGE-SCALE PIG PRODUCTION SYSTEM IN NORTHERN CHINA : A CASE STUDY

作者：Zhou, Y. Q.;Dong, H. M.;Xin, H. W.;Xin, H. W.;Zhu, Z. Z.;Huang, W. Q.;Wang, Y.

关键词：Greenhouse gas; Life cycle assessment; Mitigation; Pig
Spectral Characterization of Nitrogen in Farmland Soil by Laser-Induced Breakdown Spectroscopy

作者：Dong, D. M.;Zhao, C. J.;Zheng, W. G.;Zhao, X. D.;Jiao, L. Z.

关键词：laser-induced breakdown spectroscopy;ND:YAG laser;nitrogen;spectral characteristics
Measurement of chlorophyll in water based on laser-induced fluorescence spectroscopy: Using spiral-wound unclad optical fiber system and partial least squares regression

作者：Zhang, Z. J.;Zhao, C. J.;Dong, D. M.;Zheng, W. G.;Jiao, L. Z.;Zhao, X. D.

关键词：Chlorophyll; Fluorescence spectroscopy; Optical fiber; Preprocess; Regression

Machine vision technology for detecting the external defects of fruits - a review

作者其他论文 更多>>

CARBON FOOTPRINT ASSESSMENT OF A LARGE-SCALE PIG PRODUCTION SYSTEM IN NORTHERN CHINA : A CASE STUDY

Spectral Characterization of Nitrogen in Farmland Soil by Laser-Induced Breakdown Spectroscopy

Measurement of chlorophyll in water based on laser-induced fluorescence spectroscopy: Using spiral-wound unclad optical fiber system and partial least squares regression

意 见 箱

作者其他论文更多>>

意见箱