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Attention-Guided Wheat Disease Recognition Network through Multi-Scale Feature Optimization
Research Article | Published: 05 March 2025
IECE Transactions on Sensing, Communication, and Control Volume 2, Issue 1, 2025: 11-24
Volume 2, Issue 1, IECE Transactions on Sensing, Communication, and Control
Volume 2, Issue 1, 2025
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Xue-Bo JIN
Xue-Bo JIN
Beijing Technology and Business University, China
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IECE Transactions on Sensing, Communication, and Control, Volume 2, Issue 1, 2025: 11-24

Free to Read | Research Article | 05 March 2025
Attention-Guided Wheat Disease Recognition Network through Multi-Scale Feature Optimization
by
Niamat Ullah Niamat Ullah 1 *
Bilal Ahmad Bilal Ahmad 1
Aqib Khan Aqib Khan 2
Ismail Khan Ismail Khan 1
Ikram Majeed Khan Ikram Majeed Khan 3
Salman Khan Salman Khan 4
1 Department of Computer Science, Govt Degree College, Lalqilla Maidan, Dir Lower 18300, Pakistan
2 Department of Botany, Islamia College University, Peshawar 25000, Pakistan
3 Coventry University, Priory Street, Coventry CV1 5FB, United Kingdom
4 Codeninja Inc., Lahore, Pakistan
* Corresponding Author: Niamat Ullah, niamatabbas4@gmail.com
DOI: 10.62762/TSCC.2025.435806
Received: 11 January 2025, Accepted: 20 February 2025, Published: 05 March 2025  
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Abstract
Accurate and timely detection of wheat diseases remains crucial for sustainable agriculture, particularly in major wheat-producing regions. Wheat diseases pose a significant threat to global food security, need precise and timely detection to promote sustainable agriculture. Existing approaches consistently employ single-scale features with shallow-layered convolutional neural networks (CNNs). To bridge the research gaps, we introduce a novel Multi-Scale Wheat Disease Network (MSWDNet) with feature collaboration for wheat disease recognition supported by a comprehensive dataset collected from wheat fields. This study fills research gaps by introducing a novel technique to improve detection accuracy and promote wheat agriculture. Our network uses multistage architecture with progressive feature fusion, incorporating dilated convolution blocks and efficient channel attention mechanisms to capture both fine-grained details and broader contextual patterns. The custom dataset comprises 3,351 high-quality images across five classes collected under diverse environmental conditions. Through extensive experimentation with various CNN backbones, EfficientNet-B7 emerged as the optimal feature extractor, achieving 92.55% accuracy. Our complete architecture, enhanced with multi-scale feature integration and channel attention mechanisms, achieved 98.50% accuracy. Comprehensive ablation studies validate the effectiveness of each architectural component.
Graphical Abstract
Attention-Guided Wheat Disease Recognition Network through Multi-Scale Feature Optimization
Keywords
visual intelligence
wheat diseases
deep learning
machine vision
attention network
Funding
This work was supported without any funding.
Conflicts of Interest
Salman Khan is an employee of Codeninja Inc., Lahore, Pakistan. 
Ethical Approval and Consent to Participate
Not applicable.
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APA Style
Ullah, N., Ahmad, B., Khan, A., Khan, I., Khan, I.M., & Khan, S. (2025). Attention-Guided Wheat Disease Recognition Network through Multi-Scale Feature Optimization. IECE Transactions on Sensing, Communication, and Control, 2(1), 11–24. https://doi.org/10.62762/TSCC.2025.435806
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