Comparison of Image Extraction Model for Cocoa Disease Fruits Attack in Support Vector Machine Classification

Abstract

This study aims to compare the results of four feature extraction models in the case of early recognition of disease attacks on cocoa fruits. The image extraction models used in this study are Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), Hue Saturation Value (HSV), and Gray-level Co-occurrence Histograms (GLCH). In addition, the Support Vector Machine (SVM) model was used for the classification technique to evaluate the extraction results from the cocoa image dataset. The classification results using SVM showed the best performance on feature extraction HSV in all types of Kernel SVM used (Linear, RBF, and Polynomial), with the highest accuracy of 80.95% on RBF Kernel. Furthermore, the HSV performance in recognizing disease attacks on cocoa fruits, based on Precision, Recall, and F1-Score values, showed that, on average, HSV had a better value than other feature extraction methods.

Access to Document

Other files and links

• Link to publication in Scopus

Fingerprint