Honey Classification using Hyperspectral Imaging and Machine Learning

TL;DR

This paper presents a machine learning approach utilizing hyperspectral imaging to accurately classify honey's botanical origins, achieving over 95% accuracy, which advances automated honey authentication methods.

Contribution

The study introduces a novel combination of dataset transformation, LDA feature extraction, and SVM/KNN classifiers for honey classification using hyperspectral data, achieving state-of-the-art accuracy.

Findings

Achieved 95.13% classification accuracy with hyperspectral images.

Demonstrated the effectiveness of LDA for feature reduction.

Validated the approach on a standard honey hyperspectral dataset.

Abstract

In this paper, we propose a machine learning-based method for automatically classifying honey botanical origins. Dataset preparation, feature extraction, and classification are the three main steps of the proposed method. We use a class transformation method in the dataset preparation phase to maximize the separability across classes. The feature extraction phase employs the Linear Discriminant Analysis (LDA) technique for extracting relevant features and reducing the number of dimensions. In the classification phase, we use Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) models to classify the extracted features of honey samples into their botanical origins. We evaluate our system using a standard honey hyperspectral imaging (HSI) dataset. Experimental findings demonstrate that the proposed system produces state-of-the-art results on this dataset, achieving the highest classification accuracy of 95.13% for hyperspectral image-based classification and 92.80% for hyperspectral instance-based classification.