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Machine learning approach for classification of mangifera indica leaves using digital image analysis

Authors :
Tanveer Aslam
Salman Qadri
Syed Furqan Qadri
Syed Ali Nawaz
Abdul Razzaq
Syeda Shumaila Zarren
Mubashir Ahmad
Muzammil Ur Rehman
Amir Hussain
Israr Hussain
Javeria Jabeen
Adnan Altaf
Source :
International Journal of Food Properties, Vol 25, Iss 1, Pp 1987-1999 (2022)
Publication Year :
2022
Publisher :
Taylor & Francis Group, 2022.

Abstract

There is a wide range of horticulture farming in Asia. Mangifera Indica belongs to the species of flowering plant, also publicly recognized as mango. It has a significant local demand as well as a broad export marketplace throughout the world, and is considered as ‘King of Fruits.’ There are many mango varieties and each has its own business market. Efficient identification of the mango varieties is still difficult because of untrained growers and obsolete farming culture, especially in remote areas of the Asia. The primary purpose of this research study was to discriminate mango varieties with the potential of machine learning techniques by analyzing their leaves. For the purpose, we selected leaves of eight mango varieties, namely: Anwar-Ratul (AR), Chaunsa (CHAUN), Langra (LANG), Sindhri (SIND), Saroli (SARO), Fajri (FAJ), Desi (DESI), Alo-Marghan (ALM). A digital cell phone camera captured these datasets in open atmosphere without any well-equipped lab and infrastructure. Binary, histogram, RST, spectral, and texture features were employed for machine learning (ML)-based mango leaf image discrimination. A k-fold (k = 10) cross-validation method was used for ML classification. The k nearest neighbors (KNN) classifier achieved maximum overall classification accuracy (OCA) from 88.33% to 97%.

Details

Language :
English
ISSN :
10942912 and 15322386
Volume :
25
Issue :
1
Database :
Directory of Open Access Journals
Journal :
International Journal of Food Properties
Publication Type :
Academic Journal
Accession number :
edsdoj.836834d09fdc45148d6cb29864e86a81
Document Type :
article
Full Text :
https://doi.org/10.1080/10942912.2022.2117822