ConMamba: Contrastive Vision Mamba for Plant Disease Detection
This work addresses the problem of costly data annotation and computational inefficiency in plant disease detection for precision agriculture, offering an incremental improvement over existing self-supervised approaches.
The paper tackled plant disease detection by proposing ConMamba, a self-supervised learning framework that integrates a Vision Mamba Encoder and a dual-level contrastive loss, achieving significant performance improvements over state-of-the-art methods on three benchmark datasets.
Plant Disease Detection (PDD) is a key aspect of precision agriculture. However, existing deep learning methods often rely on extensively annotated datasets, which are time-consuming and costly to generate. Self-supervised Learning (SSL) offers a promising alternative by exploiting the abundance of unlabeled data. However, most existing SSL approaches suffer from high computational costs due to convolutional neural networks or transformer-based architectures. Additionally, they struggle to capture long-range dependencies in visual representation and rely on static loss functions that fail to align local and global features effectively. To address these challenges, we propose ConMamba, a novel SSL framework specially designed for PDD. ConMamba integrates the Vision Mamba Encoder (VME), which employs a bidirectional State Space Model (SSM) to capture long-range dependencies efficiently. Furthermore, we introduce a dual-level contrastive loss with dynamic weight adjustment to optimize local-global feature alignment. Experimental results on three benchmark datasets demonstrate that ConMamba significantly outperforms state-of-the-art methods across multiple evaluation metrics. This provides an efficient and robust solution for PDD.