📅 26 April 2024
DOI: 10.62411/jcta.10459
Enhanced Vision Transformer and Transfer Learning Approach to Improve Rice Disease Recognition
Journal of Computing Theories and Applications
Universitas Dian Nuswantoro
📄 Abstract
In the evolving landscape of agricultural technology, recognizing rice diseases through computational models is a critical challenge, predominantly addressed through Convolutional Neural Networks (CNN). However, the localized feature extraction of CNNs often falls short in complex scenarios, necessitating a shift towards models capable of global contextual understanding. Enter the Vision Transformer (ViT), a paradigm-shifting deep learning model that leverages a self-attention mechanism to transcend the limitations of CNNs by capturing image features in a comprehensive global context. This research embarks on an ambitious journey to refine and adapt the ViT Base(B) transfer learning model for the nuanced task of rice disease recognition. Through meticulous reconfiguration, layer augmentation, and hyperparameter tuning, the study tests the model's prowess across both balanced and imbalanced datasets, revealing its remarkable ability to outperform traditional CNN models, including VGG, MobileNet, and EfficientNet. The proposed ViT model not only achieved superior recall (0.9792), precision (0.9815), specificity (0.9938), f1-score (0.9791), and accuracy (0.9792) on challenging datasets but also established a new benchmark in rice disease recognition, underscoring its potential as a transformative tool in the agricultural domain. This work not only showcases the ViT model's superior performance and stability across diverse tasks and datasets but also illuminates its potential to revolutionize rice disease recognition, setting the stage for future explorations in agricultural AI applications.
🔖 Keywords
#Multi-head Attention; Paddy Disease Classification; Rice Leaves Disease Recognition; Self-Attention; Transfer Learning; Vision Transformer
ℹ️ Informasi Publikasi
Tanggal Publikasi
26 April 2024
Volume / Nomor / Tahun
Volume 1, Nomor 4, Tahun 2024
📝 HOW TO CITE
Rachman, Rahadian Kristiyanto; Setiadi, De Rosal Ignatius Moses; Susanto, Ajib; Nugroho, Kristiawan; Islam, Hussain Md Mehedul, "Enhanced Vision Transformer and Transfer Learning Approach to Improve Rice Disease Recognition," Journal of Computing Theories and Applications, vol. 1, no. 4, Apr. 2024.
ACM
ACS
APA
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
0
Sitasi
Cek Google Scholar