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CNN-HOG based hybrid feature mining for classification of coffee bean varieties using image processing

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Published in:Multimedia Tools and Applications vol. 84, no. 2 (Jan 2025), p. 749
Published:
Springer Nature B.V.
Subjects:
CNN
Feature extraction
Performance evaluation
Classification
Quality control
Radial basis function
Support vector machines
Image segmentation
Image filters
Color imagery
Gray scale
Kernel functions
Image processing
Coffee industry
Coffee
Image processing systems
Online Access:Citation/Abstract
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Abstract:Ethiopia, known as the birthplace of coffee, relies on coffee exports as a major source of foreign currency. This research paper focuses on developing a hybrid feature mining technique to automatically classify Ethiopian coffee beans based on their provenance: Harrar, Jimma, Limu, Sidama, and Wellega, which correspond to their botanical origins. A dataset of coffee bean images is collected from various regions through the Ethiopian Commodity Exchange (ECX) in Addis Ababa. The proposed system incorporates preprocessing phases including image resizing, filtering, contrast enhancement, noise removal, grayscale conversion, and segmentation using a combined thresholding and K-means approach for grayscale and RGB images, respectively. Classification is performed using a radial basis function (RBF) kernel function of support vector machine (SVM). To address the color-feature similarity challenge, the study explores merging color and texture features using the histogram of oriented gradients (HOG) local feature descriptor. Performance evaluation is conducted for HOG feature extraction, CNN feature extraction, and a hybrid feature vector (HOG-CNN) using a multi-class SVM classifier, achieving accuracies of 74.17%, 85.83%, and 97.5%, respectively. The deep-shallow-based feature (CNN-HOG) combination demonstrates the highest accuracy of 97.5% in this study. The findings highlight the effectiveness of the proposed hybrid feature mining approach in automatically classifying Ethiopian coffee bean varieties, with potential applications in quality control and traceability within the coffee industry.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-024-18952-z
Source:ABI/INFORM Global