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This study provides a thorough review of the use of Vision Transformer (ViT) models in the detection and classification of leaf diseases. It also includes preliminary results from the preprocessing stages. The paper investigates many strategies utilized in picture preprocessing, including grayscale conversion, noise reduction, and quality enhancement. These techniques are essential for optimizing the performance of ViT models. The research emphasizes the significance of performing thorough examination of histogram plots and preprocessed photos in order to enhance the accuracy of disease classification. The results emphasize the capacity of powerful AI models to revolutionize agricultural practices through accurate and automated disease diagnosis, establishing a foundation for future study and implementation in this domain.

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Enhancing Leaf Disease Detection with Vision Transformers: A Literature Survey and Preprocessing Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • V. Sandeep Kumar Reddy,
  • Kasarapu Ramani

摘要

This study provides a thorough review of the use of Vision Transformer (ViT) models in the detection and classification of leaf diseases. It also includes preliminary results from the preprocessing stages. The paper investigates many strategies utilized in picture preprocessing, including grayscale conversion, noise reduction, and quality enhancement. These techniques are essential for optimizing the performance of ViT models. The research emphasizes the significance of performing thorough examination of histogram plots and preprocessed photos in order to enhance the accuracy of disease classification. The results emphasize the capacity of powerful AI models to revolutionize agricultural practices through accurate and automated disease diagnosis, establishing a foundation for future study and implementation in this domain.