StateSpace-SSL: Linear-Time Self-supervised Learning for Plant Disease Detection
This work addresses plant disease detection for agricultural applications by improving efficiency and accuracy, though it is incremental as it adapts state-space models to a specific domain.
The paper tackled the problem of self-supervised learning for plant disease detection by proposing StateSpace-SSL, a linear-time framework using a Vision Mamba state-space encoder, which outperformed CNN- and transformer-based baselines on three datasets.
Self-supervised learning (SSL) is attractive for plant disease detection as it can exploit large collections of unlabeled leaf images, yet most existing SSL methods are built on CNNs or vision transformers that are poorly matched to agricultural imagery. CNN-based SSL struggles to capture disease patterns that evolve continuously along leaf structures, while transformer-based SSL introduces quadratic attention cost from high-resolution patches. To address these limitations, we propose StateSpace-SSL, a linear-time SSL framework that employs a Vision Mamba state-space encoder to model long-range lesion continuity through directional scanning across the leaf surface. A prototype-driven teacher-student objective aligns representations across multiple views, encouraging stable and lesion-aware features from labelled data. Experiments on three publicly available plant disease datasets show that StateSpace-SSL consistently outperforms the CNN- and transformer-based SSL baselines in various evaluation metrics. Qualitative analyses further confirm that it learns compact, lesion-focused feature maps, highlighting the advantage of linear state-space modelling for self-supervised plant disease representation learning.