A Hybrid Deep Feature Based VGG19 and SVM Approach for Durian Leaf Classification

Abstract

Durian leaf classification has remained challenging due to high visual similarity among superior durian varieties and the limited robustness of conventional convolutional neural network models that rely on Softmax classifiers. This study aimed to address this limitation by investigating a deep feature-based classification framework that combined VGG19 as a feature extractor with a Support Vector Machine classifier. The experiments were conducted on a dataset of 1,530 durian leaf images representing four varieties: Bawor, Duri Hitam, Musang King, and Super Tembaga. Four experimental scenarios were designed to evaluate classification performance using Support Vector Machine and Softmax classifiers under both imbalanced and balanced data conditions through the application of Synthetic Minority Over-sampling Technique. The research gap addressed in this study lay in the absence of prior investigations that systematically evaluated the integration of VGG19 and Support Vector Machine for durian leaf variety classification under varying data distributions. Experimental results showed that the proposed VGG19–Support Vector Machine framework consistently achieved higher accuracy and more stable performance than Softmax-based models. This study demonstrated that replacing the conventional Softmax classifier with a Support Vector Machine significantly improved classification robustness compared to previous approaches that employed end-to-end convolutional neural network architectures.