Comparison of EfficientNetB7 and MobileNetV2 in Herbal Plant Species Classification Using Convolutional Neural Networks

Abstract

This study compares the performance of EfficientNetB7 and MobileNetV2 in classifying herbal plant species using Convolutional Neural Networks (CNNs). The primary objective was to automatically identify herbal plant species with high accuracy. Based on the evaluation results, both EfficientNetB7 and MobileNetV2 achieved approximately 98% accuracy in recognizing herbal plant species. While both models demonstrated excellent performance in precision, recall, and F1-score for most plant species, EfficientNetB7 showed a slight edge in some evaluation metrics. These findings provide valuable insights into the potential implementation of CNN architectures in automatic plant recognition applications, particularly for developing widely applicable web-based systems for herbal plant identification.