A Review on Automatic Classification of Honey Botanical Origins using Machine Learning

Abstract

Honey botanical origin classification is essential to honey authentication and honey botanical origin mislabeling prevention. Recently, several researchers have used advanced analytical techniques for classifying honey floral sources. These methods have incorporated different acquisition technologies and machine learning (ML) models. In this paper, we review state-of-the-art approaches for classifying honey botanical sources. We discuss the various technologies used for measuring honey constituents, honey physical and chemical properties, and technologies for capturing honey spatial and spectral data. Also, we discuss the ML techniques and their classification performances. We give recommendations for future work at the end of this paper.