DenseSwinGNNNet: A Novel Deep Learning Framework for Accurate Turmeric Leaf Disease Classification

Seerat Singla, Gunjan Shandilya, Ayman Altameem, Ruby Pant, Ajay Kumar, Ateeq Ur Rehman, Ahmad Almogren,
DenseSwinGNNNet: A Novel Deep Learning Framework for Accurate Turmeric Leaf Disease Classification,
Phyton-International Journal of Experimental Botany,
Volume 94, Issue 12,
2025,
Pages 4021-4057,
ISSN 0031-9457,
https://doi.org/10.32604/phyton.2025.073354.
(https://www.sciencedirect.com/science/article/pii/S0031945725002199)
Abstract: Turmeric Leaf diseases pose a major threat to turmeric cultivation, causing significant yield loss and economic impact. Early and accurate identification of these diseases is essential for effective crop management and timely intervention. This study proposes DenseSwinGNNNet, a hybrid deep learning framework that integrates DenseNet-121, the Swin Transformer, and a Graph Neural Network (GNN) to enhance the classification of turmeric leaf conditions. DenseNet121 extracts discriminative low-level features, the Swin Transformer captures long-range contextual relationships through hierarchical self-attention, and the GNN models inter-feature dependencies to refine the final representation. A total of 4361 images from the Mendeley turmeric leaf dataset were used, categorized into four classes: Aphids Disease, Blotch, Leaf Spot, and Healthy Leaf. The dataset underwent extensive preprocessing, including augmentation, normalization, and resizing, to improve generalization. An 80:10:10 split was applied for training, validation, and testing respectively. Model performance was evaluated using accuracy, precision, recall, F1-score, confusion matrices, and ROC curves. Optimized with the Adam optimizer at the learning rate of 0.0001, DenseSwinGNNNet achieved an overall accuracy of 99.7%, with precision, recall, and F1-scores exceeding 99% across all classes. The ROC curves reported AUC values near 1.0, indicating excellent class separability, while the confusion matrix showed minimal misclassification. Beyond high predictive performance, the framework incorporates considerations for cybersecurity and privacy in data-driven agriculture, supporting secure data handling and robust model deployment. This work contributes a reliable and scalable approach for turmeric leaf disease detection and advances the application of AI-driven precision agriculture.
Keywords: Turmeric leaf disease; deep learning; DenseNet121; swin transformer; graph neural network (GNN); image classification