PLANXMAMBA: A hybrid CNN – MAMBA model for plant disease recognition

Abstract Image-based plant disease recognition plays a pivotal role in smart agriculture , facilitating early detection and effective management of crop diseases. Existing approaches primarily employ Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs) to extract discriminative visual features and perform disease classification, achieving encouraging outcomes. However, these models often struggle to adequately model long-range spatial dependencies and sequence-level information while maintaining lightweight architectures suitable for deployment on mobile or edge devices. In this study, we introduce Plan-tXMamba, an efficient hybrid model that synergistically integrates CNNs with a structured State Space Model (SSM), termed Mamba, to simultaneously capture local and global contextual features. The architecture is designed to enhance accuracy, interpretability, and computational efficiency. Comprehensive experiments were conducted on multiple benchmark datasets—including Maize, Rice, Apple, Embrapa, and PlantVillage—to demonstrate the effectiveness and generalizability of the proposed approach.