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Classification of Mango Varieties using Machine Learning Techniques

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Vijay C.P., Yashpal Gupta S.

Vijay C.P. and Yashpal Gupta S.. Classification of Mango Varieties using Machine Learning Techniques. International Journal of Computer Applications 183(22):16-19, August 2021. BibTeX

	author = {Vijay C.P. and Yashpal Gupta S.},
	title = {Classification of Mango Varieties using Machine Learning Techniques},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2021},
	volume = {183},
	number = {22},
	month = {Aug},
	year = {2021},
	issn = {0975-8887},
	pages = {16-19},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2021921571},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


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Machine Learning, SVM, Gaussian Filter, Bit pattern representation