Research Article
Classification of Mango Varieties using Machine Learning Techniques
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 183 - Issue 22 |
| Published: Aug 2021 |
| Authors: Vijay C.P., Yashpal Gupta S. |
10.5120/ijca2021921571
|
Vijay C.P., Yashpal Gupta S. . Classification of Mango Varieties using Machine Learning Techniques. International Journal of Computer Applications. 183, 22 (Aug 2021), 16-19. DOI=10.5120/ijca2021921571
@article{ 10.5120/ijca2021921571,
author = { Vijay C.P.,Yashpal Gupta S. },
title = { Classification of Mango Varieties using Machine Learning Techniques },
journal = { International Journal of Computer Applications },
year = { 2021 },
volume = { 183 },
number = { 22 },
pages = { 16-19 },
doi = { 10.5120/ijca2021921571 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2021
%A Vijay C.P.
%A Yashpal Gupta S.
%T Classification of Mango Varieties using Machine Learning Techniques%T
%J International Journal of Computer Applications
%V 183
%N 22
%P 16-19
%R 10.5120/ijca2021921571
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The "King of Fruits" mango is the most looked for after natural product for both immediate and backhanded utilization over the globe. Since it has extremely high fare an incentive there is a need to build up a procedure that is equipped for grouping the mangoes impartially. Any classifier exhibitions is subject to the highlights extricated from the district of enthusiasm of the example. In this paper, a similar investigation of highlight extraction techniques is made to characterize the mangoes. "Alphonso" mango cultivar was picked for the experimentation. Automation of natural product acknowledgment and order is a fascinating use of PC vision. Conventional organic product order strategies have regularly depended on manual tasks dependent on visual capacity and such techniques are monotonous, tedious and conflicting. Outer shape appearance is the principle hotspot for natural product characterization. Lately, PC machine vision and picture handling methods have been found progressively valuable in the natural product industry, particularly for applications in quality examination and shading, estimate, shape arranging.
References
- Fruits 360 Dataset on GitHub.https://github.com/Horea94/ Fruit-Images-Dataset.
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- GadgileDhondiram and Chavan Ashok “Detection of post-harvest fungal diseases of mango by X-ray scanning non-destructive technology”, Plant Pathology & Quarantine 7(1): 65–69 (2017) ISSN 2229-2217
- Ahuja Bhargava, Atul Bansal “Fruits and vegetables quality evaluation using computer vision: A review” Journal of King Saud University Computer and Information Sciences (Article in press).
- Suvarna Kanakaraddi, Prashant Iliger, Akshay Gaonkar,Minal Alagoudar, AbhinavPrakash “Analysis and Grading of Pathogenic Disease Of Chilli Fruit Using Image Processing” Proceedings of International Conference on Advances in Engineering & Technology, 20th April-2014, Goa, India, ISBN: 978-93-84209-06-3.
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