ConMamba: Contrastive Vision Mamba for Plant Disease Detection

2025/06/03 by Abdullah Al Mamun, Miaohua Zhang, Mamun, Abdullah Al +7 · 1 citation
Agricultural and Biological Sciences · Computer Science · Engineering · #Smart Agriculture and AI #Advanced Neural Network Applications #Advanced Data and IoT Technologies

paper · pdf · doi:10.48550/arxiv.2506.03213

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.

Citations

• Understanding the Benefits of SimCLR Pre-Training in Two-Layer Convolutional Neural Networks
• A comprehensive survey on contrastive learning
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• HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising
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• Exploring Simple Siamese Representation Learning
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• Searching for MobileNetV3
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• Gradient-based learning applied to document recognition

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• PSMamba: Progressive Self-supervised Vision Mamba for Plant Disease Recognition

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