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

Abstract

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.

Plant phenotyping relevance

植物病害状態を画像から認識する新規CNN–Mamba手法を開発し、複数ベンチマークで有効性・汎化性を検証しており、植物フェノタイピング手法が中心である。

Code and data availability

The paper uses five public benchmark datasets (PlantVillage, Embrapa, Apple, Maize, Rice), but these are cited prior-work datasets rather than paper-specific deposits, and no author code, models, or data availability URL is provided. The data availability statement is also inconsistent (mentions QMUL-Chair-V2/QMUL-Shoe

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