Honey dataset standard using hyperspectral imaging for machine learning problems

Abstract

Hyperspectral imaging has been rarely investigated for honey analyses, on the contrary to the optical spectroscopy which is widely investigated. The essential missing component to kick start this research is a standard honey hyperspectral images, called hypercubes, dataset. This paper proposes a systematic procedure for the preparation of the first honey hypercube dataset using hyperspectral imaging. Moreover, a scalable and flexible dataset module is introduced to ease the interaction between raw hypercube data and machine learning software. The developed dataset greatly benefits researchers to progress on the research of honey analysis including constituents prediction and types classification using hyperspectral imaging and machine learning.