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

• Published in: International journal of food properties Vol. 25; no. 1; pp. 1987 - 1999
• Main Authors: Aslam, Tanveer, Qadri, Salman, Qadri, Syed Furqan, Nawaz, Syed Ali, Razzaq, Abdul, Zarren, Syeda Shumaila, Ahmad, Mubashir, Ur Rehman, Muzammil, Hussain, Amir, Hussain, Israr, Jabeen, Javeria, Altaf, Adnan
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 31.12.2022; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Cell culture; classification; Flowering; Horticulture; Image processing; Learning algorithms; Leaves; Machine learning; Mango leaves; Texture features; Asia
• ISSN: 1094-2912; 1532-2386
• DOI: 10.1080/10942912.2022.2117822

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%.