Citation

Haque, Md. Ehsanul and Farid, Fahmid Al and Siam, Md. Kamrul and Absur, Md. Nurul and Uddin, Jia and Abdul Karim, Hezerul (2026) LeafSightX: an explainable attention-enhanced CNN fusion model for apple leaf disease identification. Frontiers in Artificial Intelligence, 8. ISSN 2624-8212

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Abstract

The rapid and precise identification of apple leaf diseases is crucial for minimizing yield loss in precision agriculture. However, many existing deep learning methods struggle to be applicable in real-world settings, are not easily interpretable, and often lack sufficient statistical validation. To address these difficulties, we propose our solution approach LeafSightX. This dual-backbone architecture combines features from DenseNet201 and InceptionV3 using Multi-Head Self-Attention (MHSA) techniques, enhancing representational capability and spatial context reasoning. Our extensive procedure includes specialized preprocessing and limited data augmentation, improving model resilience in many scenarios. Furthermore, LeafSightX integrates explainable AI techniques with Grad-CAM visualizations to improve transparency. In assessments of a five-class apple leaf disease dataset featuring field and laboratory images, LeafSightX demonstrates exceptional performance, attaining a test accuracy of 99.64%, an F1-score of 0.9962, and AUC and PR-AUC scores of 1.000, far surpassing all baseline CNNs. Cross-validated Cohen's Kappa (mean = 0.9917, σ = 0.0020) and AUC (mean = 0.9998) indicate a significant level of predictive consistency. Despite its architectural complexity, the model offers real-time inference capabilities, ensuring per-sample latency suitable for edge device deployment. Additionally, the proposed LeafSightX framework was trained and evaluated on an additional independent apple leaf disease dataset, achieving a test accuracy of 99.69%, demonstrating its robustness and generalization. Our approach is a rigorously evaluated, clear, and highly accurate system for identifying plant diseases, providing a reproducible foundation for the actual application of AI in agriculture.

Item Type:	Article
Uncontrolled Keywords:	apple leaf disease, convolutional neural networks (CNNs), DenseNet201, explainable artificial intelligence (XAI), InceptionV3, multi-head self-attention (MHSA), plant disease detection, precision agriculture
Subjects:	S Agriculture > S Agriculture (General)
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	03 Mar 2026 02:10
Last Modified:	03 Mar 2026 02:10
URII:	http://shdl.mmu.edu.my/id/eprint/15418

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