| dc.description.abstract | Tomato leaf diseases significantly reduce crop yield; therefore, early and accurate disease detection
is required. Traditional detection methods are laborious and error-prone, particularly in large-scale farms, whereas
existing hybrid deep learning models often face computational inefficiencies and poor generalization over diverse
environmental and disease conditions. This study presents a unified U-Net-Vision Mamba Model with Hierarchical
Bottleneck Attention Mechanism (U-net-Vim-HBAM), which integrates U-Net’s high-resolution segmentation, Vision
Mamba’s efficient contextual processing, and a Hierarchical Bottleneck Attention Mechanism to address the challenges
of disease detection accuracy, computational complexity, and efficiency in existing models. The model was trained
on the Tomato Leaves and PlantVillage combined datasets from Kaggle and achieved 98.63% accuracy, 98.24%
precision, 96.41% recall, and 97.31% F1 score, outperforming baseline models. Simulation tests demonstrated the model’s
compatibility across devices with computational efficacy, ensuring its potential for integration into real-time mobile
agricultural applications. The model’s adaptability to diverse datasets and conditions suggests that it is a versatile and
high-precision instrument for disease management in agriculture, supporting sustainable agricultural practices. This
offers a promising solution for crop health management and contributes to food security. | en_US |
