TY  - JOUR
AU  - Ullah, Niamat
AU  - Ahmad, Bilal
AU  - Khan, Aqib
AU  - Khan, Ismail
AU  - Khan, Ikram Majeed
AU  - Khan, Salman
PY  - 2025
DA  - 2025/03/05
TI  - Attention-Guided Wheat Disease Recognition Network through Multi-Scale Feature Optimization
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 2
IS  - 1
SP  - 11
EP  - 24
DO  - 10.62762/TSCC.2025.435806
UR  - https://www.icck.org/article/abs/TSCC.2025.435806
KW  - visual intelligence
KW  - wheat diseases
KW  - deep learning
KW  - machine vision
KW  - attention network
AB  - 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.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Ullah2025AttentionG,
  author = {Niamat Ullah and Bilal Ahmad and Aqib Khan and Ismail Khan and Ikram Majeed Khan and Salman Khan},
  title = {Attention-Guided Wheat Disease Recognition Network through Multi-Scale Feature Optimization},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2025},
  volume = {2},
  number = {1},
  pages = {11-24},
  doi = {10.62762/TSCC.2025.435806},
  url = {https://www.icck.org/article/abs/TSCC.2025.435806},
  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.},
  keywords = {visual intelligence, wheat diseases, deep learning, machine vision, attention network},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}