ConMamba: Contrastive Vision Mamba for Plant Disease Detection

Abdullah Al Mamun; Miaohua Zhang; David Ahmedt-Aristizabal; Zeeshan Hayder; Mohammad Awrangjeb

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arXiv · 2506.03213

ConMamba: Contrastive Vision Mamba for Plant Disease Detection

Abdullah Al Mamun·Miaohua Zhang·David Ahmedt-Aristizabal·Zeeshan Hayder·Mohammad Awrangjeb

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Abstract

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.

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Abdullah Al Mamun, Miaohua Zhang, David Ahmedt-Aristizabal, Zeeshan Hayder, Mohammad Awrangjeb. 2025-11-24. ConMamba: Contrastive Vision Mamba for Plant Disease Detection. https://arxiv.org/abs/2506.03213

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