Advanced Hybrid Convolutional Neural Network for Leaf-Based Plant Disease Detection

Sarmad Mahmood; Swash Mohammed; Yahya Khidhir; Tale  Saeidi; Sahar  Saleh

doi:10.22153/kej.2026.02.002

المؤلفون

Sarmad Mahmood الجامعة التقنية الشمالية https://orcid.org/0000-0001-6092-3841
Swash Mohammed الجامعة التقنية الشمالية https://orcid.org/0000-0002-7459-3182
Yahya Khidhir الجامعة التقنية الشمالية
Tale Saeidi Atlantic Technological University
Sahar Saleh Atlantic Technological University https://orcid.org/0000-0002-0015-5972

DOI:

https://doi.org/10.22153/kej.2026.02.002

الكلمات المفتاحية:

Plant disease detection; Machine learning; Convolutional neural networks; Squeeze-and-excitation (SE) blocks

الملخص

Accurate detection and classification of plant diseases are central to sustainable food production and the reduction in crop losses. Conventional identification protocols are often dependent on experts. They are time-consuming and difficult to scale. This paper proposes a novel Advanced Hybrid Convolutional Neural Network (AHCNN) model combining the attention mechanism and simplified convolution mechanism. This model can obtain high accuracy and low computational complexity. It is trained using a high-resolution leaf dataset that includes 1532 leaf images, separated into 1322 training, 150 validation and 60 test samples, with the goal of a classification of specimens into three categories: healthy, powdery and rusty. Its architecture uses squeeze-and-excitation (SE) blocks and a spatial attention mechanism, which, together, can provide enhanced feature extraction and improve the interpretability of models. Results showed that the model achieved a validation accuracy of 98% and a test accuracy of 98.33%. With a parameter number of only 0.4 million, the proposed architecture provides a lightweight solution that performs much better than conventional deep learning frameworks in terms of computational efficiency. These attributes make AHCNN an interesting candidate for real time, drone-based detection of plant diseases as part of precision agriculture systems.

التنزيلات

تنزيل البيانات ليس متاحًا بعد.

المراجع

[1] S. O. Araujo, R. S. Peres, J. C. Ramalho, F. Lidon, and J. Barata, "Machine learning applications in agriculture: current trends, challenges, and future perspectives," Agronomy, vol. 13, p. 2976, 2023, https://doi.org/10.3390/agronomy13122976.

[2] I. Ahmed and P. K. Yadav, "Plant disease detection using machine learning approaches," Expert Systems, vol. 40, p. e13136, 2023, https://doi.org/10.1111/exsy.13136.

[3] M. Sharma, C. J. Kumar, and A. Deka, "Early diagnosis of rice plant disease using machine learning techniques," Archives of Phytopathology and Plant Protection, vol. 55, pp. 259-283, 2022, https://doi.org/10.1080/03235408.2021.2015866.

[4] A. Singla, A. Nehra, K. Joshi, A. Kumar, N. Tuteja, R. K. Varshney, et al., "Exploration of machine learning approaches for automated crop disease detection," Current plant biology, vol. 40, p. 100382, 2024, https://doi.org/10.1016/j.cpb.2024.100382.

[5] A. V. Panchal, S. C. Patel, K. Bagyalakshmi, P. Kumar, I. R. Khan, and M. Soni, "Image-based plant diseases detection using deep learning," Materials Today: Proceedings, vol. 80, pp. 3500-3506, 2023, https://doi.org/10.1016/j.cpb.2024.100382.

[6] J. Kotwal, R. Kashyap, and S. Pathan, "Agricultural plant diseases identification: From traditional approach to deep learning," Materials Today: Proceedings, vol. 80, pp. 344-356, 2023, https://doi.org/10.1016/j.matpr.2023.02.370.

[7] M. Arsenovic, M. Karanovic, S. Sladojevic, A. Anderla, and D. Stefanovic, "Solving current limitations of deep learning based approaches for plant disease detection," Symmetry, vol. 11, p. 939, 2019, https://doi.org/10.3390/sym11070939.

[8] J. Liu and X. Wang, "Plant diseases and pests detection based on deep learning: a review," Plant methods, vol. 17, p. 22, 2021, https://doi.org/10.1186/s13007-021-00722-9.

[9] J. A. Wani, S. Sharma, M. Muzamil, S. Ahmed, S. Sharma, and S. Singh, "Machine learning and deep learning based computational techniques in automatic agricultural diseases detection: Methodologies, applications, and challenges," Archives of Computational methods in Engineering, vol. 29, pp. 641-677, 2022, https://doi.org/10.1007/s11831-021-09588-5.

[10] R. Rahman. Plant disease recognition dataset, Kaggle. [Online]. Available: https://www.kaggle.com/datasets/rashikrahmanpritom/plant-disease-recognition-dataset. [Accessed: Nov. 12, 2024].

[11] S. S. Mohammed and H. G. Clarke, "Conditional image-to-image translation generative adversarial network (cGAN) for fabric defect data augmentation," Neural Computing and Applications, vol. 36, pp. 20231-20244, 2024, https://doi.org/10.1007/s00521-024-10179-1.

[12] S. N. Mahmood, S. S. Mohammed, A. G. Ismaeel, H. G. Clarke, I. N. Mahmood, D. A. Aziz, et al., "Improved malaria cells detection using deep convolutional neural network," in 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), 2023, pp. 1-4, https://doi.org/10.1109/HORA58378.2023.10156747.

[13] S. S. Mohammed, H. G. Clarke, and S. N. Mahmood, "A fully convolutional neural network for fast detection, classification, and segmentation of fabric defects," Neural Computing and Applications, vol. 37, pp. 23249-23272, 2025, https://doi.org/10.1007/s00521-025-11495-w.

[14] J. G. Barbedo, "Factors influencing the use of deep learning for plant disease recognition," Biosystems engineering, vol. 172, pp. 84-91, 2018, https://doi.org/10.1016/j.biosystemseng.2018.05.013.

[15] K. P. Ferentinos, "Deep learning models for plant disease detection and diagnosis," Computers and electronics in agriculture, vol. 145, pp. 311-318, 2018, https://doi.org/10.1016/j.compag.2018.01.009.

[16] S. S. Hari, M. Sivakumar, P. Renuga, and S. Suriya, "Detection of plant disease by leaf image using convolutional neural network," in 2019 International conference on vision towards emerging trends in communication and networking (ViTECoN), 2019, pp. 1-5, https://doi.org/10.1109/ViTECoN.2019.8899748.

[17] S. V. Militante, B. D. Gerardo, and N. V. Dionisio, "Plant leaf detection and disease recognition using deep learning," in 2019 IEEE Eurasia conference on IOT, communication and engineering (ECICE), 2019, pp. 579-582, https://doi.org/10.1109/ECICE47484.2019.8942686.

[18] M. H. Saleem, J. Potgieter, and K. M. Arif, "Plant disease detection and classification by deep learning," Plants, vol. 8, p. 468, 2019, https://doi.org/10.3390/plants8110468.

[19] M. R. Ullah, N. A. Dola, A. Sattar, and A. Hasnat, "Plant diseases recognition using machine learning," in 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), 2019, pp. 67-73, https://doi.org/10.1109/SMART46866.2019.9117284.

[20] R. Sujatha, J. M. Chatterjee, N. Jhanjhi, and S. N. Brohi, "Performance of deep learning vs machine learning in plant leaf disease detection," Microprocessors and Microsystems, vol. 80, p. 103615, 2021, https://doi.org/10.1016/j.compag.2018.01.009.

[21] R. Z. Khaleel, H. Z. Khaleel, A. A. Abdullah Al-Hareeri, A. S. Mahdi Al-Obaidi, and A. J. Humaidi, "Improved Trajectory Planning of Mobile Robot Based on Pelican Optimization Algorithm," Journal Européen des Systèmes Automatisés, vol. 57, 2024, https://doi.org/10.18280/jesa.570408.

[22] H. Z. Khaleel and A. J. Humaidi, "Towards accuracy improvement in solution of inverse kinematic problem in redundant robot: A comparative analysis," International Review of Applied Sciences and Engineering, vol. 15, pp. 242-251, 2024, https://doi.org/10.1556/1848.2023.00722.

[23] H. Obied, M. K. Al-Taleb, H. Z. Khaleel, and A. F. AbdulKareem, "Implementation and Derivation Kinematics Modeling Analysis of WidowX 250 6Degreef of Freedom Robotic Arm," Journal of Engineering and Sustainable Development, vol. 29, pp. 473-484, 2025, https://doi.org/10.31272/jeasd.2454.

[24] İ. Yağ and A. Altan, "Artificial intelligence-based robust hybrid algorithm design and implementation for real-time detection of plant diseases in agricultural environments," Biology, vol. 11, p. 1732, 2022, https://doi.org/10.3390/biology11121732.

[25] S. R. Krishnan, E. P. Sim, C. M. Varghese, B. Rajan, S. Chippy, and E. Thomas, "Detection of Diseases in Tomato Leaves Using Deep Learning Models: A Survey," in 2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST), 2024, pp. 1-6, https://doi.org/10.1109/ICTEST60614.2024.10576182.

[26] M. Venkatanaresh and I. Kullayamma, "Deep learning based concurrent excited gated recurrent unit for crop recommendation based on soil and climatic conditions," Multimedia Tools and Applications, pp. 1-30, 2024, https://doi.org/10.1007/s11042-023-18004-y.

[27] W. Albattah, M. Nawaz, A. Javed, M. Masood, and S. Albahli, "A novel deep learning method for detection and classification of plant diseases," Complex & Intelligent Systems, pp. 1-18, 2022, https://doi.org/10.1007/s40747-021-00536-1.

[28] C. Sarkar, D. Gupta, U. Gupta, and B. B. Hazarika, "Leaf disease detection using machine learning and deep learning: Review and challenges," Applied Soft Computing, vol. 145, p. 110534, 2023, https://doi.org/10.1016/j.asoc.2023.110534.

[29] S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, "Cbam: Convolutional block attention module," in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 3-19, https://doi.org/10.1007/978-3-030-01234-2_1.