Journal of Agricultural Machinery
with the collaboration of Iranian Society of Mechanical Engineers (ISME)

Transformer-based Models for foliar Maize Diseases Classification in Real Field Conditions: Case Study of Kandahar, Afghanistan

Articles in Press

Document Type : Research Article- En

Authors

R. Rafi 1
A. Soleimanipour 2
A. Rezaei Asl 2

1 Agricultural Engineering Department, Plant Science Faculty, Afghanistan National Agricultural Science and Technology University (ANASTU), Kandahar, Afghanistan

2 Department of Biosystem Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

https://doi.org/10.22067/jam.2026.97474.1465
Abstract
Accurate and timely detection of maize (Zea mays L.) foliar diseases is essential for improving crop productivity and ensuring food security. This study evaluates the performance of state-of-the-art deep learning models for automated maize leaf disease classification using a real-field dataset collected from agricultural farms in Kandahar Province, Afghanistan. The dataset was acquired under natural field conditions, preserving complex backgrounds, variable illumination, and diverse leaf orientations to reflect practical farming environments. Multiple advanced transformer-based deep learning architectures, including Vision Transformer (ViT), and hybrid Multi-axis Vision Transformer (MaxViT-tiny, MaxViT-small, and MaxViT-base) were investigated and compared with EfficientNet (B5, B6, and B7) models. Experimental results demonstrate that modern CNN-based and transformer-based architectures achieve up to 97% accuracy, despite challenging real-world conditions. Among the evaluated models, EfficientNet-B7, ViT, and MaxViT-Tiny attained the highest accuracy of 97%, with MaxViT-Tiny recording the highest precision (0.9741), indicating exceptional class discrimination, particularly for challenging Spot and Blight classes under real-field variability. Minor misclassifications were primarily observed between visually similar disease classes, while overall precision and recall remained consistently high. The findings confirm the effectiveness of hybrid and attention-based architectures for real-world maize disease detection and demonstrate their feasibility for real-world agricultural applications. By leveraging authentic field data, this study provides practical and ecologically valid insights that support early disease diagnosis, reduced manual inspection, and improved sustainable crop management.

Keywords

EfficientNet
Field dataset
leaf disease
Maize
MaxViT
ViT
Subjects

Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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Journal of Agricultural Machinery

Articles in Press, Accepted Manuscript
Available Online from 23 May 2026

  • Receive Date 19 January 2026
  • Revise Date 15 February 2026
  • Accept Date 18 April 2026
  • First Publish Date 23 May 2026

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