Plant Image Classification Using Deep Learning with Xception Architecture and Fine-Tuning Techniques

Muhammad Amirul Mustofa; Panca Dear; Afif Zainul Muttaqin

doi:10.55927/esa.v4i4.87

Authors

Muhammad Amirul Mustofa Universitas Nahdlatul Ulama Lampung
Panca Dear Universitas Nahdlatul Ulama Lampung
Afif Zainul Muttaqin Universitas Nahdlatul Ulama Lampung

DOI:

https://doi.org/10.55927/esa.v4i4.87

Keywords:

Image Classification, Deep Learning, Xception, Fine-Tuning, Plant Identification

Abstract

The decline in public knowledge about medicinal plants in Indonesia threatens cultural preservation and the utilization of biodiversity. This community service project aims to bridge this gap by developing a prototype of an automated plant identification tool. The community service process focused on designing and training an Artificial Intelligence-based system. The method used was deep learning with the Xception architecture, implementing transfer learning and fine-tuning strategies for optimization. All activities, from data collection to model testing, were carried out from September to December 2024. The result was a classification model with 98.05% accuracy. Consequently, this model has great potential to be developed into a publicly accessible application, supporting the preservation of traditional knowledge and botanical education.

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