用线性时序模型提升植物病害自监督学习效果
StateSpace-SSL: Linear-Time Self-supervised Learning for Plant Disease Detection
- 采用状态空间模型方向扫描叶片，捕捉病斑连续演化特征
- 在三个数据集上均优于卷积与变压器基线方法
- 适合需要高效、精准病害识别的农业视觉任务
自监督学习（SSL）可利用大量未标注叶片图像，但现有基于CNN或视觉变换器的方法难以适配农业图像。基于CNN的SSL难以捕捉沿叶面连续演化的病斑模式，而基于变压器的SSL因高分辨率块带来二次注意力开销。为此，我们提出StateSpace-SSL，一种线性时间自监督框架，采用视觉马尔可夫状态空间编码器，通过方向性扫描建模叶片表面长程病变连续性。原型驱动的师生目标对齐多视角表征，促进从标注数据中学习稳定且病灶感知的特征。在三个公开植物病害数据集上的实验表明，StateSpace-SSL在多种评估指标下持续优于基于CNN和变压器的基线方法。定性分析进一步证实其学习到紧凑、聚焦病灶的特征图，凸显线性状态空间建模在自监督植物病害表征学习中的优势。
原文摘要 · Abstract (English)
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.
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