Open Access

ARTICLE

A Unified U-Net-Vision Mamba Model with Hierarchical Bottleneck Attention for Detection of Tomato Leaf Diseases

Geoffry Mutiso^*, John Ndia

School of Computing and Information Technology, Murang’a University of Technology, Murang’a, 75-10200, Kenya

* Corresponding Author: Geoffry Mutiso. Email:

(This article belongs to the Special Issue: Advances in Artificial Intelligence for Engineering and Sciences)

Journal on Artificial Intelligence 2025, 7, 275-288. https://doi.org/10.32604/jai.2025.069768

Received 30 June 2025; Accepted 11 August 2025; Issue published 05 September 2025

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.

---

Keywords

---