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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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Enhanced wheat crop leaf disease classification using multi-level contrast enhancement and modified vision

Irfan Haider¹, Muhammad Nazir², Sajid Ali Khan³

¹Department of Computer Science, HITEC University Taxila, Taxila, Pakistan.

Scientific Reports

|November 14, 2025

Summary

This study introduces an advanced AI method for detecting wheat crop diseases using enhanced images and Vision Transformers (ViTs). The innovative approach achieves high accuracy, improving disease diagnosis for better crop yields.

Keywords:

Agricultural disease detection Deep learning Multi-Level contrast enhancement Vision transformer (ViT)Wheat crop leaf disease

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Area of Science:

Agricultural Science
Computer Vision
Plant Pathology

Background:

Wheat is crucial for global food security but vulnerable to diseases impacting yield and quality.
Early and accurate disease detection is essential for effective management and yield optimization.

Purpose of the Study:

To develop and evaluate an innovative approach for rapid and precise diagnosis of wheat crop leaf diseases.
To combine multi-level contrast enhancement with a novel transformer-based architecture for improved disease detection.

Main Methods:

Utilized a multi-level contrast enhancement framework to improve wheat crop image quality.
Employed Vision Transformers (ViTs) for efficient, multi-scale feature extraction and dimensionality reduction.
Evaluated three ViT variants (modified seven-block, ViT-16-Tiny, modified seven-block with skip connections) on public wheat datasets.

Main Results:

Achieved high classification accuracies: 98.90% (modified seven-block ViT), 97.50% (ViT-16-Tiny), and 97.90% (modified seven-block with skip connections).
Demonstrated superior performance compared to state-of-the-art methods in accuracy, precision, sensitivity, and False Negative Rate.
Showcased reduced total learnable parameters compared to existing techniques.

Conclusions:

The proposed contrast enhancement and ViT-based approach offers a highly accurate and efficient solution for wheat disease diagnosis.
This AI-driven method has the potential to significantly enhance agricultural productivity and food security.
The study highlights the effectiveness of advanced deep learning techniques in addressing critical challenges in crop disease management.