ConMamba: Contrastive Vision Mamba for Plant Disease Detection

• URL: http://arxiv.org/abs/2506.03213v1
• Date: Tue, 03 Jun 2025 03:01:38 GMT
• Title: ConMamba: Contrastive Vision Mamba for Plant Disease Detection
• Authors: Abdullah Al Mamun, Miaohua Zhang, David Ahmedt-Aristizabal, Zeeshan Hayder, Mohammad Awrangjeb,
• Abstract summary: Plant Disease Detection (PDD) is a key aspect of precision agriculture.<n>Existing deep learning methods often rely on extensively annotated datasets.<n>We propose ConMamba, a novel framework specially designed for PDD.
• Score: 3.60543005189868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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