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

Abstract

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%.