FCA-ResNet: An Improved Model with Enhanced Multi-Scale Feature Fusion and Coordinate Attention for Wheat Leaf Disease Classification

Hongyan Zang; Annie Anak Joseph; Shourong Zhang; Haiyan Fu; Lili Huang; Zhen Huang

doi:10.46604/ijeti.2024.14304

Authors

Hongyan Zang Faculty of Engineering, Universiti Malaysia Sarawak, Sarawak, Malaysia/ Shandong Vanform High Energy Physics Technology Co. Ltd., Jinan, China
Annie Anak Joseph Faculty of Engineering, Universiti Malaysia Sarawak, Sarawak, Malaysia
Shourong Zhang Shandong Vanform High Energy Physics Technology Co. Ltd., Jinan, China
Haiyan Fu College of Computer and Information Engineering, Qilu Institute of Technology, Jinan, China
Lili Huang College of Computer and Information Engineering, Qilu Institute of Technology, Jinan, China
Zhen Huang College of Computer and Information Engineering, Qilu Institute of Technology, Jinan, China

DOI:

https://doi.org/10.46604/ijeti.2024.14304

Keywords:

convolutional neural networks, wheat leaf disease classification, coordinate attention, multi-scale feature fusion

Abstract

Rapid and accurate identification of leaf disease is essential in intelligent agriculture. Current methods often struggle with balancing precision and speed. This research introduces the fusion coordinate attention and residual network (FCA-ResNet) model to improve classification accuracy while maintaining a lightweight structure for both healthy wheat leaves and five common wheat leaf diseases. FCA-ResNet incorporates a coordinate attention (CA) mechanism along with a multi-branch Inception module. The model consists of an Inception-based multi-branch structure and CA mechanism fusion module, which optimizes feature focus and weight allocation. Additionally, a multi-scale fusion module utilizes both channel and spatial attention mechanisms to effectively integrate shallow and deep features, improving the detection accuracy of small lesions. The multi-branch structure is designed to replace traditional multi-layer convolution, resulting in a lightweight model. The model achieves an average accuracy of 91.6% on custom datasets, demonstrating its effectiveness in plant disease detection for agriculture.

References

Y. Liu, G. Gao, and Z. Zhang, “Crop Disease Recognition Based on Modified Light-Weight CNN with Attention Mechanism,” IEEE Access, vol. 10, pp. 112066-112075, 2022.

S. Gupta and A.K. Tripathi, “Fruit and Vegetable Disease Detection and Classification: Recent Trends, Challenges, and Future Opportunities,” Engineering Applications of Artificial Intelligence, vol. 133, part C, article no. 108260, 2024.

S. Huang, G. Zhou, M. He, A. Chen, W. Zhang, and Y. Hu, “Detection of Peach Disease Image Based on Asymptotic Non-Local Means and PCNN-IPELM,” IEEE Access, vol. 8, pp. 136421-136433, 2020.

E. Hirani, V. Magotra, J. Jain, and P. Bide, “Plant Disease Detection Using Deep Learning,” 6th International Conference for Convergence in Technology, pp. 1-4, 2021.

S. M. Hassan and A. K. Maji, “Plant Disease Identification Using a Novel Convolutional Neural Network,” IEEE Access, vol. 10, pp. 5390-5401, 2022.

P. Bedi and P. Gole, “Plant Disease Detection Using Hybrid Model Based on Convolutional Autoencoder and Convolutional Neural Network,” Artificial Intelligence in Agriculture, vol. 5, pp. 90-101, 2021.

V. Kukreja, R. Sharma, V. Sharma, and A. Verma, “Crop Vigil: Automated Wheat Bunt Disease Multi-Classification with a CNN-RNN Hybrid Model and Attention Block,” 14th International Conference on Computing Communication and Networking Technologies, pp. 1-6, 2023.

I. Haider, M. A. Khan, M. Nazir, A. Hamza, O. Alqahtani, M. T. H. Alouane, et al., “Crops Leaf Disease Recognition from Digital and RS Imaging Using Fusion of Multi Self-Attention RBNet Deep Architectures and Modified Dragonfly Optimization,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 7260-7277, 2024.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.

G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700-4708, 2017.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al., “Going Deeper with Convolutions,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015.

R. Maurya, S. Mahapatra, and L. Rajput, “A Lightweight Meta-Ensemble Approach for Plant Disease Detection Suitable for IoT-Based Environments,” IEEE Access, vol. 12, pp. 28096-28108, 2024.

H. Sun, H. Xu, B. Liu, D. He, J. He, H. Zhang, et al., “MEAN-SSD: A Novel Real-Time Detector for Apple Leaf Diseases Using Improved Light-Weight Convolutional Neural Networks,” Computers and Electronics in Agriculture, vol. 189, article no. 106379, 2021.

P. Kumar, S. Raghavendran, K. Silambarasan, K. S. Kannan, and N. Krishnan, “Mobile Application Using DCDM and Cloud-Based Automatic Plant Disease Detection,” Environmental Monitoring and Assessment, vol. 195, no. 1, article no. 44, 2023.

M. Aziz Hosen Foysal, F. Ahmed, and M. Zahurul Haque, “Multi-Class Plant Leaf Disease Detection: A CNN-Based Approach with Mobile App Integration,” International Journal of Computer Applications, vol. 186, no. 41, pp. 62-68, 2024.

L. Goyal, C. M. Sharma, A. Singh, and P. K. Singh, “Leaf and Spike Wheat Disease Detection & Classification Using an Improved Deep Convolutional Architecture,” Informatics in Medicine Unlocked, vol. 25, article no. 100642, 2021.

M. A. Genaev, E. S. Skolotneva, E. I. Gultyaeva, E. A. Orlova, N. P. Bechtold, and D. A. Afonnikov, “Image-Based Wheat Fungi Diseases Identification by Deep Learning,” Plants, vol. 10, no. 8, article no. 1500, 2021.

T. Hayit, H. Erbay, F. Varçın, F. Hayit, and N. Akci, “Determination of the Severity Level of Yellow Rust Disease in Wheat by Using Convolutional Neural Networks,” Journal of Plant Pathology, vol. 103, no. 3, pp. 923-934, 2021.

J. Jiang, H. Liu, C. Zhao, H. He, J. Ma, T. Cheng, et al., “Evaluation of Diverse Convolutional Neural Networks and Training Strategies for Wheat Leaf Disease Identification with Field-Acquired Photographs,” Remote Sensing, vol. 14, no. 14, article no. 3446, 2022.

R. Mao, Y. Zhang, Z. Wang, X. Hao, T. Zhu, S. Gao, et al., “Dae-Mask: A Novel Deep-Learning-Based Automatic Detection Model for In-Field Wheat Diseases,” Precision Agriculture, vol. 25, no. 2, pp. 785-810, 2024.

S. Nigam, R. Jain, S. Marwaha, A. Arora, V. K. Singh, A. K. Singh, et al., “Automating Yellow Rust Disease Identification in Wheat Using Artificial Intelligence,” Indian Journal of Agricultural Sciences, vol. 91, no. 9, pp. 1391-1395, 2021.

O. Getch, “Wheat Leaf Dataset,” https://www.kaggle.com/datasets/olyadgetch/wheat-leaf-dataset, 2021.

S. Dunk, “Wheat Disease Detection,” https://www.kaggle.com/datasets/sinadunk23/behzad-safari-jalal, 2020.

L. Xu, B. Cao, F. Zhao, S. Ning, P. Xu, W. Zhang, et al., “Wheat Leaf Disease Identification Based on Deep Learning Algorithms,” Physiological and Molecular Plant Pathology, vol. 123, article no. 101940, 2023.

F. Xu, G. Zhang, C. Song, H. Wang, and S. Mei, “Multiscale and Cross-Level Attention Learning for Hyperspectral Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 61, article no. 5501615, 2023.

Y. Feng, C. Liu, J. Han, Q. Lu, and X. Xing, “IRB-5-CA Net: A Lightweight, Deep Learning-Based Approach to Wheat Seed Identification,” IEEE Access, vol. 11, pp. 119553-119559, 2023.

X. Zhu, J. Li, R. Jia, B. Liu, Z. Yao, and A. Yuan, “LAD-Net: A Novel Light Weight Model for Early Apple Leaf Pests and Diseases Classification,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 20, no. 2, pp. 1156-1169, 2023.

H. Yu, X. Cheng, Z. Li, Q. Cai, and C. Bi, “Disease Recognition of Apple Leaf Using Lightweight Multi-Scale Network with ECANet,” Computer Modeling in Engineering & Sciences, vol. 132, no. 3, pp. 711-738, 2022.

W. Bao, X. Yang, D. Liang, G. Hu, and X. Yang, “Lightweight Convolutional Neural Network Model for Field Wheat Ear Disease Identification,” Computers and Electronics in Agriculture, vol. 189, article no. 106367, 2021.

Z. Xiao, Y. Shi, G. Zhu, J. Xiong, and J. Wu, “Leaf Disease Detection Based on Lightweight Deep Residual Network and Attention Mechanism,” IEEE Access, vol. 11, pp. 48248-48258, 2023.