Honey Classification using Hyperspectral Imaging and Machine Learning
This addresses the need for automated honey origin classification, but it is incremental as it applies existing methods like LDA, SVM, and KNN to a specific dataset.
The paper tackles the problem of classifying honey botanical origins by proposing a machine learning method using hyperspectral imaging, achieving state-of-the-art classification accuracies of 95.13% for image-based and 92.80% for instance-based classification.
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.