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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, [email protected]
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.
References
  1. Poole, N., Donovan, J., & Erenstein, O. (2021). Agri-nutrition research: revisiting the contribution of maize and wheat to human nutrition and health. Food Policy, 100, 101976.
    [CrossRef]   [Google Scholar]
  2. Sabença, C., Ribeiro, M., Sousa, T., Poeta, P., Bagulho, A. S., & Igrejas, G. (2021). Wheat/gluten-related disorders and gluten-free diet misconceptions: A review. Foods, 10(8), 1765.
    [CrossRef]   [Google Scholar]
  3. Chai, Y., Senay, S., Horvath, D., & Pardey, P. (2022). Multi-peril pathogen risks to global wheat production: A probabilistic loss and investment assessment. Frontiers in Plant Science, 13, 1034600.
    [CrossRef]   [Google Scholar]
  4. Biel, W., Jaroszewska, A., Stankowski, S., Sobolewska, M., & Kępińska-Pacelik, J. (2021). Comparison of yield, chemical composition and farinograph properties of common and ancient wheat grains. European Food Research and Technology, 247(6), 1525–1538.
    [CrossRef]   [Google Scholar]
  5. Kloppe, T., Boshoff, W., Pretorius, Z., Lesch, D., Akin, B., Morgounov, A., ... & Cowger, C. (2022). Virulence of Blumeria graminis f. sp. tritici in Brazil, South Africa, Turkey, Russia, and Australia. Frontiers in Plant Science, 13, 954958.
    [CrossRef]   [Google Scholar]
  6. Mahum, R., Munir, H., Mughal, Z. U., Awais, M., Khan, F. S., Saqlain, M., ... & Tlili, I. (2023). A novel framework for potato leaf disease detection using an efficient deep learning model. Human and Ecological Risk Assessment: An International Journal, 29(2), 303–326.
    [CrossRef]   [Google Scholar]
  7. Weng, Y., Wu, J., Kelly, T., & Johnson, W. (2024). Comprehensive overview of artificial intelligence applications in modern industries. arXiv preprint arXiv:2409.13059.
    [CrossRef]   [Google Scholar]
  8. Jahan, N., Flores, P., Liu, Z., Friskop, A., Mathew, J. J., & Zhang, Z. (2020). Detecting and distinguishing wheat diseases using image processing and machine learning algorithms. In 2020 ASABE Annual International Virtual Meeting (p. 1). ASABE.
    [CrossRef]   [Google Scholar]
  9. Dixit, A., & Nema, S. (2018). Wheat leaf disease detection using machine learning method-a review. International Journal of Computer Science and Mobile Computing, 7(5), 124–129.
    [Google Scholar]
  10. Sood, S., & Singh, H. (2020). An implementation and analysis of deep learning models for the detection of wheat rust disease. In 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) (pp. 341–347). IEEE.
    [CrossRef]   [Google Scholar]
  11. Xu, L., Cao, B., Zhao, F., Ning, S., Xu, P., Zhang, W., & Hou, X. (2023). Wheat leaf disease identification based on deep learning algorithms. Physiological and Molecular Plant Pathology, 123, 101940.
    [CrossRef]   [Google Scholar]
  12. Reis, H. C., & Turk, V. (2024). Integrated deep learning and ensemble learning model for deep feature-based wheat disease detection. Microchemical Journal, 197, 109790.
    [CrossRef]   [Google Scholar]
  13. Alafeef, M., Moitra, P., & Pan, D. (2020). Nano-enabled sensing approaches for pathogenic bacterial detection. Biosensors and Bioelectronics, 165, 112276.
    [CrossRef]   [Google Scholar]
  14. Treboux, J., & Genoud, D. (2018). Improved machine learning methodology for high precision agriculture. In 2018 Global Internet of Things Summit (GIoTS) (pp. 1–6). IEEE.
    [CrossRef]   [Google Scholar]
  15. Rumpf, T., Mahlein, A. K., Steiner, U., Oerke, E. C., Dehne, H. W., & Plümer, L. (2010). Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1), 91–99.
    [CrossRef]   [Google Scholar]
  16. Ramesh, S., Hebbar, R., Niveditha, M., Pooja, R., Shashank, N., & Vinod, P. V. (2018). Plant disease detection using machine learning. In 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C) (pp. 41–45). IEEE.
    [CrossRef]   [Google Scholar]
  17. Phadikar, S., Sil, J., & Das, A. K. (2012). Classification of rice leaf diseases based on morphological changes. International Journal of Information and Electronics Engineering, 2(3), 460–463.
    [Google Scholar]
  18. Prajapati, H. B., Shah, J. P., & Dabhi, V. K. (2017). Detection and classification of rice plant diseases. Intelligent Decision Technologies, 11(3), 357–373.
    [CrossRef]   [Google Scholar]
  19. Ahmed, K., Shahidi, T. R., Alam, S. M. I., & Momen, S. (2019). Rice leaf disease detection using machine learning techniques. In 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI) (pp. 1–5). IEEE.
    [CrossRef]   [Google Scholar]
  20. Panigrahi, K. P., Das, H., Sahoo, A. K., & Moharana, S. C. (2020). Maize leaf disease detection and classification using machine learning algorithms. In Progress in Computing, Analytics and Networking: Proceedings of ICCAN 2019 (pp. 659–669). Springer.
    [CrossRef]   [Google Scholar]
  21. Waghmare, H., Kokare, R., & Dandawate, Y. (2016). Detection and classification of diseases of grape plant using opposite colour local binary pattern feature and machine learning for automated decision support system. In 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 513–518). IEEE.
    [CrossRef]   [Google Scholar]
  22. Zhao, J., Fang, Y., Chu, G., Yan, H., Hu, L., & Huang, L. (2020). Identification of leaf-scale wheat powdery mildew (Blumeria graminis f. sp. Tritici) combining hyperspectral imaging and an SVM classifier. Plants, 9(8), 936.
    [CrossRef]   [Google Scholar]
  23. Liu, Z., Zhang, W., Shu, S., & Jin, X. (2013). Image recognition of wheat disease based on RBF support vector machine. In 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013) (pp. 307-310). Atlantis Press.
    [CrossRef]   [Google Scholar]
  24. Xu, P., Wu, G., Guo, Y., Yang, H., & Zhang, R. (2017). Automatic wheat leaf rust detection and grading diagnosis via embedded image processing system. Procedia Computer Science, 107, 836–841.
    [CrossRef]   [Google Scholar]
  25. Ennadifi, E., Laraba, S., Vincke, D., Mercatoris, B., & Gosselin, B. (2020). Wheat diseases classification and localization using convolutional neural networks and GradCAM visualization. In 2020 International Conference on Intelligent Systems and Computer Vision (ISCV) (pp. 1–5). IEEE.
    [CrossRef]   [Google Scholar]
  26. Zhou, L., Zhang, C., Taha, M. F., Wei, X., He, Y., & Qiu, Z. (2020). Wheat kernel variety identification based on a large near-infrared spectral dataset and a novel deep learning-based feature selection method. Frontiers in Plant Science, 11, 575810.
    [CrossRef]   [Google Scholar]
  27. Ashraf, M., Abrar, M., Qadeer, N., Alshdadi, A. A., Sabbah, T., & Khan, M. A. (2023). A Convolutional Neural Network Model for Wheat Crop Disease Prediction. Computers, Materials & Continua, 75(2), 1–15.
    [Google Scholar]
  28. Baranwal, S., Khandelwal, S., & Arora, A. (2019). Deep learning convolutional neural network for apple leaves disease detection. In Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM). https://dx.doi.org/10.2139/ssrn.3351641
    [Google Scholar]
  29. Lakshmi, R. K., & Savarimuthu, N. (2021). A novel transfer learning ensemble based deep neural network for plant disease detection. In 2021 International Conference on Computational Performance Evaluation (ComPE) (pp. 017–022). IEEE.
    [CrossRef]   [Google Scholar]
  30. Zhang, S., Zhang, S., Zhang, C., Wang, X., & Shi, Y. (2019). Cucumber leaf disease identification with global pooling dilated convolutional neural network. Computers and Electronics in Agriculture, 162, 422–430.
    [CrossRef]   [Google Scholar]
  31. Dang, L. M., Hassan, S. I., Suhyeon, I., Sangaiah, A. K., Mehmood, I., ... & Moon, H. (2020). UAV based wilt detection system via convolutional neural networks. Sustainable Computing: Informatics and Systems, 28, 100250.
    [CrossRef]   [Google Scholar]
  32. Ha, J. G., Moon, H., Kwak, J. T., Hassan, S. I., Dang, M., ... & Park, H. Y. (2017). Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles. Journal of Applied Remote Sensing, 11(4), 042621.
    [CrossRef]   [Google Scholar]
  33. Kurmi, Y., Saxena, P., Kirar, B. S., Gangwar, S., Chaurasia, V., & Goel, A. (2022). Deep CNN model for crops’ diseases detection using leaf images. Multidimensional Systems and Signal Processing, 33(3), 981–1000.
    [CrossRef]   [Google Scholar]
  34. Karlekar, A., & Seal, A. (2020). SoyNet: Soybean leaf diseases classification. Computers and Electronics in Agriculture, 172, 105342.
    [CrossRef]   [Google Scholar]
  35. Islam, M., Aloraini, M., Habib, S., Alanazi, M. D., Khan, I., & Khan, A. (2024). Optimal Features Driven Attention Network with Medium-Scale Benchmark for Wheat Diseases Recognition. IEEE Access.
    [CrossRef]   [Google Scholar]
  36. Ur Rehman, I., Ullah, I., Khan, H., Guellil, M. S., Koo, J., ... & Lee, M. Y. (2024). A comprehensive systematic literature review of ML in nanotechnology for sustainable development. Nanotechnology Reviews, 13(1), 20240069.
    [CrossRef]   [Google Scholar]
  37. Ullah, I., Adhikari, D., Khan, H., Anwar, M. S., Ahmad, S., & Bai, X. (2024). Mobile robot localization: Current challenges and future prospective. Computer Science Review, 53, 100651.
    [CrossRef]   [Google Scholar]
  38. Baharom, A. K., Abdul-Rahman, S., Jamali, R., & Mutalib, S. (2020). Towards modelling autonomous mobile robot localization by using sensor fusion algorithms. In 2020 IEEE 10th International Conference on System Engineering and Technology (ICSET) (pp. 185-190). IEEE.
    [CrossRef]   [Google Scholar]
  39. Zhang, D., Lin, F., Huang, Y., & Zhang, L. (2016). Detection of wheat powdery mildew by differentiating background factors using hyperspectral imaging. International Journal of Agriculture & Biology, 18(4), 747–756.
    [Google Scholar]
  40. Zhang, J., Pu, R., Huang, W., Yuan, L., Luo, J., & Wang, J. (2012). Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses. Field Crops Research, 134, 165–174.
    [CrossRef]   [Google Scholar]
  41. Khan, I. H., Liu, H., Li, W., Cao, A., Wang, X., Liu, H., ... & Cao, W. (2021). Early detection of powdery mildew disease and accurate quantification of its severity using hyperspectral images in wheat. Remote Sensing, 13(18), 3612.
    [CrossRef]   [Google Scholar]
  42. Wang, H., Qin, F., Liu, Q., Ruan, L., Wang, R., Ma, Z., ... & Cheng, P. (2015). Identification and disease index inversion of wheat stripe rust and wheat leaf rust based on hyperspectral data at canopy level. Journal of Spectroscopy, 2015, 651810.
    [CrossRef]   [Google Scholar]
  43. Bao, W., Zhao, J., Hu, G., Zhang, D., Huang, L., & Liang, D. (2021). Identification of wheat leaf diseases and their severity based on elliptical-maximum margin criterion metric learning. Sustainable Computing: Informatics and Systems, 30, 100526.
    [CrossRef]   [Google Scholar]
  44. Aboneh, T., Rorissa, A., Srinivasagan, R., & Gemechu, A. (2021). Computer vision framework for wheat disease identification and classification using Jetson GPU infrastructure. Technologies, 9(3), 47.
    [CrossRef]   [Google Scholar]
  45. Liu, X., Zhou, S., Chen, S., Yi, Z., Pan, H., & Yao, R. (2022). Buckwheat disease recognition based on convolution neural network. Applied Sciences, 12(9), 4795.
    [CrossRef]   [Google Scholar]
  46. Jin, X., Jie, L., Wang, S., Qi, H. J., & Li, S. W. (2018). Classifying wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in the wild field. Remote Sensing, 10(3), 395.
    [CrossRef]   [Google Scholar]
  47. Deng, J., Lv, X., Yang, L., Zhao, B., Zhou, C., ... & Shi, J. (2022). Assessing macro disease index of wheat stripe rust based on SegFormer with complex background in the field. Sensors, 22(15), 5676.
    [CrossRef]   [Google Scholar]
  48. Su, W. H., Zhang, J., Yang, C., Page, R., Szinyei, T., ... & Steffenson, B. J. (2020). Automatic evaluation of wheat resistance to fusarium head blight using dual mask-RCNN deep learning frameworks in computer vision. Remote Sensing, 13(1), 26.
    [CrossRef]   [Google Scholar]
  49. Shafi, U., Mumtaz, R., Qureshi, M. D. M., Mahmood, Z., Tanveer, S. K., ... & Zaidi, S. M. H. (2023). Embedded AI for wheat yellow rust infection type classification. IEEE Access, 11, 23726–23738.
    [CrossRef]   [Google Scholar]
  50. Huang, H., Deng, J., Lan, Y., Yang, A., Zhang, L., ... & Deng, Y. (2019). Detection of helminthosporium leaf blotch disease based on UAV imagery. Applied Sciences, 9(3), 558.
    [CrossRef]   [Google Scholar]
  51. Pan, Q., Gao, M., Wu, P., Yan, J., & Li, S. (2021). A deep-learning-based approach for wheat yellow rust disease recognition from unmanned aerial vehicle images. Sensors, 21(19), 6540.
    [CrossRef]   [Google Scholar]
  52. Mi, Z., Zhang, X., Su, J., Han, D., & Su, B. (2020). Wheat stripe rust grading by deep learning with attention mechanism and images from mobile devices. Frontiers in Plant Science, 11, 558126.
    [CrossRef]   [Google Scholar]
  53. Bao, W., Yang, X., Liang, D., Hu, G., & Yang, X. (2021). Lightweight convolutional neural network model for field wheat ear disease identification. Computers and Electronics in Agriculture, 189, 106367.
    [CrossRef]   [Google Scholar]
  54. Kong, J., Wang, H., Yang, C., Jin, X., Zuo, M., & Zhang, X. (2022). A spatial feature-enhanced attention neural network with high-order pooling representation for application in pest and disease recognition. Agriculture, 12(4), 500.
    [CrossRef]   [Google Scholar]
  55. Kong, J., Wang, H., Wang, X., Jin, X., Fang, X., & Lin, S. (2021). Multi-stream hybrid architecture based on cross-level fusion strategy for fine-grained crop species recognition in precision agriculture. Computers and Electronics in Agriculture, 185, 106134.
    [CrossRef]   [Google Scholar]
  56. Shrestha, G., Das, M., & Dey, N. (2020). Plant disease detection using CNN. In 2020 IEEE Applied Signal Processing Conference (ASPCON) (pp. 109–113). IEEE.
    [CrossRef]   [Google Scholar]
  57. Yue, H., Guo, J., Yin, X., Zhang, Y., Zheng, S., Zhang, Z., & Li, C. (2022). Salient object detection in low-light images via functional optimization-inspired feature polishing. Knowledge-Based Systems, 257, 109938.
    [CrossRef]   [Google Scholar]
  58. Usman, M. T., Khan, H., Singh, S. K., Lee, M. Y., & Koo, J. (2024). Efficient deepfake detection via layer-frozen assisted dual attention network for consumer imaging devices. IEEE Transactions on Consumer Electronics.
    [CrossRef]   [Google Scholar]
  59. Yu, X., Park, S., Kim, D., Kim, E., Kim, J., Kim, W., ... & Xiong, S. (2023). A practical wearable fall detection system based on tiny convolutional neural networks. Biomedical Signal Processing and Control, 86, 105325.
    [CrossRef]   [Google Scholar]
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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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