Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis

Dameshwari Sahu; Ravindra Manohar Potdar

doi:doi:10.11648/j.ajai.20170101.12

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Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis

Dameshwari Sahu, Ravindra Manohar Potdar

Published in American Journal of Artificial Intelligence (Volume 1, Issue 1)

Received: 2 May 2017 Accepted: 16 May 2017 Published: 11 July 2017

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Abstract

The image processing and computer vision systems have been widely used for identification, classification, grading and quality evaluation in the agriculture area. Defect identification and maturity detection of mango fruits are challenging task for the computer vision to achieve near human levels of recognition. The proposed framework is useful in the supermarkets and can be utilized in computer vision for the automatic sorting of fruits from a set, consisting of different kind of fruits. The objective of this work is to develop an automated tool, which can be capable of identifying defect and detect maturity of mango fruits based on shape, size and color features by digital image analysis. MATLAB have been used as the programming tool for identification and classification of fruits using Image Processing toolbox. Proposed method can be used to detect the visible defects, stems, size and shape of mangos, and to grade the mango in high speed and precision.

Published in	American Journal of Artificial Intelligence (Volume 1, Issue 1)
DOI	10.11648/j.ajai.20170101.12
Page(s)	5-14
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Defect Identification, Agriculture Image Processing, Image Moment, Mango Fruit, Maturity Detection

References

[1]	C. S. Nandi, B. Tudu, and C. Koley, “A machine vision-based maturity prediction system for sorting of harvested mangoes,” IEEE Trans. Instrum. Meas., vol. 63, no. 7, pp. 1722–1730, 2014.
[2]	C. Prieto and D. Carolina, “Classification of Oranges by Maturity, Using Image Processing Techniques,” in Proceedings of 3rd IEEE International Congress of Engineering Mechatronics and Automation (CIIMA), 2014, pp. 1–5.
[3]	A. Rocha, D. C. Hauagge, J. Wainer, and S. Goldenstein, “Automatic fruit and vegetable classification from images,” Comput. Electron. Agric., vol. 70, no. 1, pp. 96–104, 2010.
[4]	S. R. Dubey and A. S. Jalal, “Application of Image Processing in Fruit and Vegetable Analysis: A Review,” J. Intell. Syst., vol. 24, no. 4, pp. 405–424, 2015.
[5]	A. M. Aibinu, M. J. E. Salami, A. A. Shafie, N. Hazali, and N. Termidzi, “Automatic Fruits Identification System Using Hybrid Technique,” in Proceedings of 6th IEEE International Symposium on Electronic Design, Test and Application, 2011, pp. 217–221.
[6]	E. A. Murillo-Bracamontes, M. E. Martinez-Rosas, M. M. Miranda-Velasco, H. L. Martinez-Reyes, J. R. Martinez-Sandoval, and H. Cervantes-De-Avila, “Implementation of Hough Transform for fruit image segmentation,” in Procedia Engineering, 2012, vol. 35, pp. 230–239.
[7]	V. Pham and B. Lee, “An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm,” Vietnam J. Comput. Sci., vol. 2, no. 1, pp. 25–33, 2015.
[8]	A. Gongal, S. Amatya, M. Karkee, Q. Zhang, and K. Lewis, “Sensors and systems for fruit detection and localization: A review,” Comput. Electron. Agric., vol. 116, pp. 8–19, 2015.
[9]	T. Meruliya, “Image Processing for Fruit Shape and Texture Feature Extraction - Review,” Int. J. Comput. Appl. (0975, vol. 129, no. 8, pp. 30–33, 2015.
[10]	S. Poorani and P. G. Brindha, “Automatic detection of pomegranate fruits using K-mean clustering,” Int. J. Adv. Res. Sci. Eng., vol. 3, no. 8, pp. 198–202, 2014.
[11]	“Image database: Mango ‘Kent,’” 2014. [Online]. Available: http://www.cofilab.com/portfolio/mangoesdb/. [Accessed: 01-Jul-2016].
[12]	U. K. Sahu and D. Patra, “Shape Features for Image-Based Servo-Control using Image Moments,” in Proceedings of Annual IEEE India Conference (INDICON), 2015, pp. 1–6.
[13]	D. Sahu and C. Dewangan, “Identification and Classification of Mango Fruits Using Image Processing,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 2, no. 2, pp. 203–210, 2017.

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APA Style

Dameshwari Sahu, Ravindra Manohar Potdar. (2017). Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis. American Journal of Artificial Intelligence, 1(1), 5-14. https://doi.org/10.11648/j.ajai.20170101.12

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ACS Style

Dameshwari Sahu; Ravindra Manohar Potdar. Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis. Am. J. Artif. Intell. 2017, 1(1), 5-14. doi: 10.11648/j.ajai.20170101.12

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AMA Style

Dameshwari Sahu, Ravindra Manohar Potdar. Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis. Am J Artif Intell. 2017;1(1):5-14. doi: 10.11648/j.ajai.20170101.12

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@article{10.11648/j.ajai.20170101.12,
  author = {Dameshwari Sahu and Ravindra Manohar Potdar},
  title = {Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis},
  journal = {American Journal of Artificial Intelligence},
  volume = {1},
  number = {1},
  pages = {5-14},
  doi = {10.11648/j.ajai.20170101.12},
  url = {https://doi.org/10.11648/j.ajai.20170101.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20170101.12},
  abstract = {The image processing and computer vision systems have been widely used for identification, classification, grading and quality evaluation in the agriculture area. Defect identification and maturity detection of mango fruits are challenging task for the computer vision to achieve near human levels of recognition. The proposed framework is useful in the supermarkets and can be utilized in computer vision for the automatic sorting of fruits from a set, consisting of different kind of fruits. The objective of this work is to develop an automated tool, which can be capable of identifying defect and detect maturity of mango fruits based on shape, size and color features by digital image analysis. MATLAB have been used as the programming tool for identification and classification of fruits using Image Processing toolbox. Proposed method can be used to detect the visible defects, stems, size and shape of mangos, and to grade the mango in high speed and precision.},
 year = {2017}
}

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TY  - JOUR
T1  - Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis
AU  - Dameshwari Sahu
AU  - Ravindra Manohar Potdar
Y1  - 2017/07/11
PY  - 2017
N1  - https://doi.org/10.11648/j.ajai.20170101.12
DO  - 10.11648/j.ajai.20170101.12
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 5
EP  - 14
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20170101.12
AB  - The image processing and computer vision systems have been widely used for identification, classification, grading and quality evaluation in the agriculture area. Defect identification and maturity detection of mango fruits are challenging task for the computer vision to achieve near human levels of recognition. The proposed framework is useful in the supermarkets and can be utilized in computer vision for the automatic sorting of fruits from a set, consisting of different kind of fruits. The objective of this work is to develop an automated tool, which can be capable of identifying defect and detect maturity of mango fruits based on shape, size and color features by digital image analysis. MATLAB have been used as the programming tool for identification and classification of fruits using Image Processing toolbox. Proposed method can be used to detect the visible defects, stems, size and shape of mangos, and to grade the mango in high speed and precision.
VL  - 1
IS  - 1
ER  -

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Author Information

Dameshwari Sahu

Department of Electronics and Telecommunication, Bhilai Institute of Technology Durg, Chhattisgarh, India
Ravindra Manohar Potdar

Department of Electronics and Telecommunication, Bhilai Institute of Technology Durg, Chhattisgarh, India

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APA Style

Dameshwari Sahu, Ravindra Manohar Potdar. (2017). Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis. American Journal of Artificial Intelligence, 1(1), 5-14. https://doi.org/10.11648/j.ajai.20170101.12

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ACS Style

Dameshwari Sahu; Ravindra Manohar Potdar. Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis. Am. J. Artif. Intell. 2017, 1(1), 5-14. doi: 10.11648/j.ajai.20170101.12

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AMA Style

Dameshwari Sahu, Ravindra Manohar Potdar. Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis. Am J Artif Intell. 2017;1(1):5-14. doi: 10.11648/j.ajai.20170101.12

Copy | Download

@article{10.11648/j.ajai.20170101.12,
  author = {Dameshwari Sahu and Ravindra Manohar Potdar},
  title = {Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis},
  journal = {American Journal of Artificial Intelligence},
  volume = {1},
  number = {1},
  pages = {5-14},
  doi = {10.11648/j.ajai.20170101.12},
  url = {https://doi.org/10.11648/j.ajai.20170101.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20170101.12},
  abstract = {The image processing and computer vision systems have been widely used for identification, classification, grading and quality evaluation in the agriculture area. Defect identification and maturity detection of mango fruits are challenging task for the computer vision to achieve near human levels of recognition. The proposed framework is useful in the supermarkets and can be utilized in computer vision for the automatic sorting of fruits from a set, consisting of different kind of fruits. The objective of this work is to develop an automated tool, which can be capable of identifying defect and detect maturity of mango fruits based on shape, size and color features by digital image analysis. MATLAB have been used as the programming tool for identification and classification of fruits using Image Processing toolbox. Proposed method can be used to detect the visible defects, stems, size and shape of mangos, and to grade the mango in high speed and precision.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis
AU  - Dameshwari Sahu
AU  - Ravindra Manohar Potdar
Y1  - 2017/07/11
PY  - 2017
N1  - https://doi.org/10.11648/j.ajai.20170101.12
DO  - 10.11648/j.ajai.20170101.12
T2  - American Journal of Artificial Intelligence
JF  - American Journal of Artificial Intelligence
JO  - American Journal of Artificial Intelligence
SP  - 5
EP  - 14
PB  - Science Publishing Group
SN  - 2639-9733
UR  - https://doi.org/10.11648/j.ajai.20170101.12
AB  - The image processing and computer vision systems have been widely used for identification, classification, grading and quality evaluation in the agriculture area. Defect identification and maturity detection of mango fruits are challenging task for the computer vision to achieve near human levels of recognition. The proposed framework is useful in the supermarkets and can be utilized in computer vision for the automatic sorting of fruits from a set, consisting of different kind of fruits. The objective of this work is to develop an automated tool, which can be capable of identifying defect and detect maturity of mango fruits based on shape, size and color features by digital image analysis. MATLAB have been used as the programming tool for identification and classification of fruits using Image Processing toolbox. Proposed method can be used to detect the visible defects, stems, size and shape of mangos, and to grade the mango in high speed and precision.
VL  - 1
IS  - 1
ER  -

Copy | Download