Web-Based Application Design Using Vision Transformer (ViT) for Plant Leaf Disease Classification
Perancangan Aplikasi Berbasis Web Menggunakan Vision Transformer (ViT) untuk Klasifikasi Penyakit Daun Tanaman
DOI:
https://doi.org/10.21070/ups.11786Keywords:
Vision Transformer, Medicinal Plant Leaf Classification, Deep Learning, image classificationAbstract
Plant leaf disease is one of the major factors reducing agricultural productivity, yet the conventional visual identification method used by farmers remains subjective, time-consuming, and dependent on limited expert availability. Most prior studies address this problem using Convolutional Neural Network (CNN)-based architectures, which are inherently limited by their local receptive field and therefore struggle to capture global spatial context in complex disease patterns. This study proposes a Vision Transformer (ViT), specifically the ViT-Small/16 (ViT-S/16) variant, to classify plant leaf disease using the Open Leaf Image Dataset I (OLID I), which contains 4,749 leaf images across 57 unique classes from eight major crop species.
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