# LeafSightX: an explainable attention-enhanced CNN fusion model for apple leaf disease identification

## TL;DR

LeafSightX is a highly accurate and interpretable AI model for identifying apple leaf diseases, suitable for real-world agricultural use.

## Contribution

LeafSightX introduces an explainable attention-enhanced CNN fusion model with strong performance and real-time inference capabilities for plant disease identification.

## Key findings

- LeafSightX achieved a test accuracy of 99.64% on a five-class apple leaf disease dataset.
- The model demonstrated real-time inference latency suitable for edge device deployment.
- Cross-validated metrics showed high predictive consistency with a Cohen's Kappa of 0.9917.

## 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.