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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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A novel attention based vision transformer optimized with hybrid optimization algorithm for turmeric leaf disease

R Selvaraj¹, M S Geetha Devasena²

¹Department of Computer Science and Engineering, Dr.N.G.P. Institute of Technology, Coimbatore, Tamil Nadu, 641048, India. selvaraj.r2029@gmail.com.

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|May 18, 2025

Summary

A new model combining Vision Transformer (ViT) with hybrid Falcon-Bowerbird Optimization (FBO) significantly improves turmeric leaf disease detection. This approach enhances accuracy, crucial for maintaining crop health and optimizing turmeric yield.

Keywords:

Bowerbird optimization Falcon optimization Hybrid optimization Self-attention mechanism Turmeric leaf disease detection Vision transformer

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Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Turmeric leaf disease detection is vital for crop health and yield optimization.
Existing methods struggle with complex leaf features, leading to lower accuracy and reliability.
Limitations in feature extraction hinder real-world applications of current turmeric disease detection models.

Purpose of the Study:

To develop a novel leaf disease detection model for turmeric.
To overcome limitations of existing methods in feature extraction and classification accuracy.
To enhance the overall performance and reliability of turmeric leaf disease detection.

Main Methods:

A hybrid model integrating Vision Transformer (ViT) with hybrid Falcon-Bowerbird Optimization (FBO) was proposed.
Turmeric images were preprocessed using histogram equalization, and then divided into patches for ViT processing.
ViT utilized a self-attention mechanism for relevant patch tokenization, while FBO optimized hyperparameters for improved convergence and performance.

Main Results:

The proposed hybrid optimized ViT model achieved an accuracy of 97.03% in turmeric leaf disease detection.
Performance evaluation included precision, recall, and F1-score on a turmeric leaf disease dataset.
The model outperformed AlexNet (95.5% accuracy) and optimized MobileNetv3 (96.8% accuracy).

Conclusions:

The novel hybrid optimized ViT model demonstrates superior performance in turmeric leaf disease detection.
The integration of ViT and FBO effectively addresses limitations in feature extraction and classification accuracy.
This advanced approach offers improved reliability for real-world turmeric crop monitoring and management.