Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis
American Journal of Artificial Intelligence
Volume 1, Issue 1, December 2017, Pages: 5-14
Received: May 2, 2017; Accepted: May 16, 2017; Published: Jul. 11, 2017
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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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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.
Defect Identification, Agriculture Image Processing, Image Moment, Mango Fruit, Maturity Detection
To cite this article
Dameshwari Sahu, Ravindra Manohar Potdar, Defect Identification and Maturity Detection of Mango Fruits Using Image Analysis, American Journal of Artificial Intelligence. Vol. 1, No. 1, 2017, pp. 5-14. doi: 10.11648/j.ajai.20170101.12
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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