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Efficient deep learning framework for arecanut disease detection using graph neural network and Bat algorithm
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DOI：10.1038/s41598-026-46535-5.png)
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Abstract
En 中文
Early and accurate detection of plant diseases remains challenging due to real-field variability (e.g., illumination variations, complex backgrounds, occlusion), irregular disease patterns (e.g., subtle or non-local symptoms), limited labeled data for niche crops like arecanut, and generalization issues in uncontrolled environments. Traditional manual inspections are labor-intensive and error-prone, while existing deep learning methods—primarily grid-based CNNs and Vision Transformers—often suffer from limited spatial modeling of non-Euclidean relationships, higher computational costs, and reduced robustness to field conditions. This research introduces a novel deep learning framework for automated detection of diseases in arecanut plants, combining Graph Neural Networks (GNNs) for capturing long-range spatial relationships in leaf images with the Bat Algorithm (BA) for efficient hyperparameter optimization. The framework utilizes a curated balanced subset of 1000 arecanut images from a larger Kaggle dataset (originally 8847 images), captured under natural farm conditions and encompassing nine disease categories. Experimental results demonstrate that the proposed GNN-BA (GB) model achieves 98.45% accuracy, 96.90% precision, 94.21% recall, and 95.05% F1-score—outperforming baselines such as CNN-ViT (93.25% accuracy) and CBAM (94.10% accuracy) by 4–5% on average, while offering lower computational overhead. The model exhibits robustness across diverse environmental and leaf variations, providing a scalable, efficient solution for real-time disease monitoring to enable timely interventions and reduce crop losses in arecanut farming.
Keywords:
Computational biology and bioinformatics
Mathematics and computing
Plant sciences
Deep learning
Graph neural networks (GNNs)
Bat algorithm (BA)
Hyperparameter optimization
Plant pathology
Arecanut disease detection
Precision agriculture
Science
Humanities and Social Sciences
multidisciplinary
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