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Related Experiment Video

Updated: Sep 10, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition.

Sk Mahmudul Hassan1, Kumar Sekhar Roy2, Ruhul Amin Hazarika2

  • 1Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India. mahmudul.hassan@manipal.edu.

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|August 23, 2025
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Summary

Accurate plant disease identification is crucial for crop protection. This study introduces an Inception-Enhanced Vision Transformer (IEViT) model that effectively identifies plant diseases using both lab and real-world images, outperforming existing methods.

Keywords:
Deep learningMachine learningPlant diseaseVision transformer

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Accurate plant disease identification is vital for crop yield and food security.
  • Manual identification is labor-intensive and requires specialized expertise.
  • Existing computer vision models often struggle with real-world field conditions.

Purpose of the Study:

  • To develop a robust deep learning architecture for plant disease identification.
  • To address the limitations of current models in handling diverse image conditions (lab vs. real-field).
  • To improve the accuracy and efficiency of automated plant disease diagnosis.

Main Methods:

  • Proposed an Inception-Enhanced Vision Transformer (IEViT) architecture.
  • IEViT integrates local and global feature extraction capabilities.
  • Utilized multiple filters with varying kernel sizes for efficient feature learning.
  • Conducted experiments on five diverse datasets, including lab and real-field images.

Main Results:

  • The IEViT model demonstrated superior performance compared to state-of-the-art deep learning models.
  • Achieved high accuracy rates across multiple datasets: 99.23% (apple leaf), 99.70% (rice), 97.02% (i-bean), 76.51% (cassava leaf), and 99.41% (plantvillage).
  • The proposed architecture achieved these results with fewer parameters than existing models.

Conclusions:

  • The Inception-Enhanced Vision Transformer (IEViT) is a robust and efficient architecture for plant disease identification.
  • IEViT effectively handles variations in image conditions, performing well on both laboratory and real-field data.
  • This approach offers a promising solution for automated, accurate, and scalable plant disease diagnosis in agriculture.