Multi-Head Self-Attention Fusion Network for Enhanced Multi-Class Crop Disease Classification

Kinyanjui, Thomas Njoroge; Mugoye, Kelvin; Kibuku, Rachael

doi:10.22044/jadm.2025.15689.2687

Document Type : Original/Review Paper

Authors

¹ Computer Science & Informatics Department, School of Pure and Applied Science, Karatina University, Kenya.

² Software Development & Information Systems, School of Technology, KCA University, Nairobi, Kenya

10.22044/jadm.2025.15689.2687

Abstract

This paper presents a Multi-Head Self-Attention Fusion Network (MHSA-FN) for real-time crop disease classification, addressing key limitations in existing models, including suboptimal feature extraction, inefficient feature recalibration, and weak multi-scale fusion. Unlike prior works that rely solely on CNNs or transformers, MHSA-FN integrates MobileNetV2, EfficientNetV2, and Vision Transformers (ViTs) with a structured multi-level attention framework for enhanced feature learning. A gated fusion mechanism and a Multiscale Fusion Module (MSFM) optimize local texture details and global spatial relationships. The model was trained on a combined dataset of PlantVillage and locally collected images, improving adaptability to real-world conditions. It achieved 98.66% training accuracy and 99.0% test accuracy across 76 disease classes, with 99.34% precision, 99.01% recall, and 99.04% F1 score. McNemar’s test (p = 0.125) and Bayesian superiority probability (0.851) validated its robustness. Confidence variance analysis (0.000010) outperformed existing models, demonstrating MHSA-FN as a scalable, high-performance AI solution for precision agriculture in resource-constrained environments.

Keywords

Main Subjects

H.3. Artificial Intelligence

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Journal of AI and Data Mining

Multi-Head Self-Attention Fusion Network for Enhanced Multi-Class Crop Disease Classification

References

References

Volume 13, Issue 2
April 2025
Pages 227-240

Multi-Head Self-Attention Fusion Network for Enhanced Multi-Class Crop Disease Classification

References

References

Volume 13, Issue 2April 2025Pages 227-240

Volume 13, Issue 2
April 2025
Pages 227-240