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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Aguiar, Fernanda Pereira Leite
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Computer Science & IT Engineering

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Enhanced classification of aromatic herbs using EfficientNet and transfer learning Antunes, Samira Nascimento; Divino, Madalena De Oliveira Barbosa; Cordeiro, Luana Dos Santos; Aguiar, Fernanda Pereira Leite; Okano, Marcelo Tsuguio
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4123-4136

Abstract

Herbs have long been used for culinary and medicinal purposes, as well as in religious rituals, due to their essential oils and aromatic properties. However, distinguishing between aromatic and medicinal herbs based on visual characteristics alone can be challenging. With recent advances in computer vision, plant identification from images has seen significant growth, offering promising applications in several domains. This article aims to evaluate the classification of aromatic herbs using the EfficientNet convolutional neural network (CNN) technique with transfer learning. The methodology used is experimental research, systematically manipulating variables to observe their effects on the object of study. The researcher plays an active role in this process, rather than being a passive observer. Based on the results and the literature review, it is evident that the objective of this research was achieved, as despite the opportunities for improvement in training to achieve accuracy above 0.8, it was possible to evaluate the classification of aromatic herbs using EfficientNet CNN through the transfer learning technique.
Co-Authors Antunes, Samira Nascimento Cordeiro, Luana Dos Santos Divino, Madalena De Oliveira Barbosa Okano, Marcelo Tsuguio