Abstract Medicinal plants including Ocimum tenuiflorum L. (Tulsi), Azadirachta indica A. Juss. (Neem), and Kalanchoe pinnata (Lam. ) Pers. (Patharkuchi) are essential sources of bioactive compounds, yet leaf diseases threaten their yield and phytochemical integrity. This study proposes LSeTNet, a lightweight hybrid CNN (Convolutional Neural Network) Transformer architecture with Squeeze-and-Excitation (SE) blocks, achieving 99. 72% accuracy, 1. 00 macro F1-score, and AUC = 1. 00 across 12 disease classes (1, 000 images/class post-augmentation) using only 9. 38 M parameters and 2. 50 GFLOPs. Five-fold cross-validation yielded 99. 74% ± 0. 14% accuracy, with rapid convergence and no overfitting. Explainable Artificial Intelligence (XAI) via Gradient-weighted Class Activation Mapping (Grad-CAM) (mean intensity: 0. 1664–0. 2702), Local Interpretable Model-agnostic Explanations (LIME), and t-distributed Stochastic Neighbor Embedding (t-SNE) (silhouette score: 0. 87) confirmed biologically meaningful attention on pathological regions. External validation on the independent BD-MediLeaves dataset (8 classes, 8, 000 samples) achieved 99. 42% accuracy and 0. 99 macro F1. With 6. 98 ms/image inference latency and 35. 81 MB memory, LSeTNet enables real-time, edge-based deployment. It significantly outperforms DenseNet169 (95. 56%), ViT-B16 (95. 61%), and LW-CNN+SE (95. 39%) (p < 10^-7, paired t-tests), establishing a transparent, efficient, and generalizable benchmark for precision phytopathology and sustainable medicinal plant cultivation.
Ahmmed et al. (Wed,) studied this question.