An explainable multimodal framework for early plant disease detection through deep learning and molecular data integration

Abstract

Early and accurate plant disease diagnosis is essential for sustainable crop production and effective disease management. This study presents an Explainable Gradient-Based Convolutional Neural Network (EG-CNN) that integrates leaf image data with gene expression and metabolomics information to enhance disease classification while providing transparent and interpretable predictions. The proposed framework was evaluated on four major plant diseases, powdery mildew, blight, anthracnose, and leaf spot, using a multimodal dataset comprising 10,000 images and associated omics data. Comparative analysis against Traditional CNN, ResNet-50, Vision Transformer (ViT), and CNN-LSTM models demonstrated the superior performance of EG-CNN. The model achieved 97.4% accuracy, 97.1% precision, 96.8% recall, and a 96.9% F1-score, outperforming all benchmark approaches. Paired t-test results revealed statistically significant improvements (p 0.05) over competing models. Confusion matrix analysis indicated minimal misclassification across disease classes, while ROC analysis produced near-perfect AUC values, confirming excellent class separability, sensitivity, and specificity. Five-fold cross-validation further demonstrated robust generalization, with accuracy ranging from 97.1% to 97.6%. Grad-CAM visualizations successfully identified disease-relevant regions, including lesions, discoloration, and necrotic tissues, thereby enhancing model transparency and user trust. The integration of omics data also improved biological interpretability by linking predictions to underlying molecular responses. In conclusion, the proposed EG-CNN framework provides a highly accurate, robust, and explainable solution for plant disease diagnosis. Its multimodal architecture and strong generalization capability make it a promising tool for precision agriculture and real-time disease monitoring applications.

Plant phenotyping relevance

植物葉画像から病害症状を推定する説明可能な深層学習フレームワークを開発し、複数モデルとの比較、交差検証、Grad-CAMによる病徴領域の評価を行っており、植物表現型取得・判定手法が中心である。

Code and data availability

The article describes a multimodal dataset (10,000 leaf images plus gene expression and metabolomics data) and an EG-CNN model, but no block contains any data availability statement, public repository deposit, or code release. The dataset and trained model are described only narratively, with no authors' public URL or,

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