View in EDS

CEA-Net: A multi-modal model for corn disease classification with dynamic fusion and cross-layer connection mechanism.

Saved in:
Bibliographic Details
Title: CEA-Net: A multi-modal model for corn disease classification with dynamic fusion and cross-layer connection mechanism.
Authors: Wang, Haoyang1 (AUTHOR), Zhou, Guoxiong1 (AUTHOR) zhougx01@163.com, Chen, Guiyun1 (AUTHOR)
Source: Pattern Recognition. May2026, Vol. 173, pN.PAG-N.PAG. 1p.
Subjects: Corn diseases, Deep learning, Machine learning, Feature extraction, Image processing
Abstract: • A cross-layer connection model is proposed for image processing. • Efficient dynamic attention fusion is proposed for multi-modal feature fusion. • Adaptive adversarial cross-entropy meta-learning is proposed to pre-train the model. Corn is one of the most widely cultivated crops globally, yet it remains highly susceptible to a variety of diseases. With the rapid advancement of deep learning, image-based methods for corn disease classification have emerged and achieved promising results. However, many existing approaches still face challenges such as reliance on single-source information and limited feature extraction capacity. To address these issues, this paper proposes a multi-modal model named CEA-Net. First, we introduce a Cross-layer Connection Model (CCM) for image processing, which integrates multi-level wavelet blocks, VMamba, and Transformer components through a cross-layer connectivity mechanism. This design enhances spatial information reorganization and facilitates efficient feature extraction and reuse within the visual backbone network. Second, we propose an Efficient Dynamic Attention Fusion (EDAF) module for multi-modal feature fusion. EDAF dynamically modulates the contribution of each modality, emphasizing dominant sources while efficiently enhancing the representational capability of feature maps. Finally, we introduce Adaptive Adversarial Cross-Entropy Meta-learning (AACEM) for model pre-training. By combining meta-learning with sharpness-aware minimization and utilizing adaptive adversarial cross-entropy loss, AACEM improves both generalization and overall performance. Experimental results show that CEA-Net achieves an accuracy of 97.40%, outperforming networks such as EfficientViM and D2R by margins of 0.81%, 0.56%, 0.67%, and 0.55% across various metrics, demonstrating its significant practical value in corn disease management. Our code and dataset are available at: https://github.com/yiyuynanodesu/CEA-Net. [ABSTRACT FROM AUTHOR]
Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:• A cross-layer connection model is proposed for image processing. • Efficient dynamic attention fusion is proposed for multi-modal feature fusion. • Adaptive adversarial cross-entropy meta-learning is proposed to pre-train the model. Corn is one of the most widely cultivated crops globally, yet it remains highly susceptible to a variety of diseases. With the rapid advancement of deep learning, image-based methods for corn disease classification have emerged and achieved promising results. However, many existing approaches still face challenges such as reliance on single-source information and limited feature extraction capacity. To address these issues, this paper proposes a multi-modal model named CEA-Net. First, we introduce a Cross-layer Connection Model (CCM) for image processing, which integrates multi-level wavelet blocks, VMamba, and Transformer components through a cross-layer connectivity mechanism. This design enhances spatial information reorganization and facilitates efficient feature extraction and reuse within the visual backbone network. Second, we propose an Efficient Dynamic Attention Fusion (EDAF) module for multi-modal feature fusion. EDAF dynamically modulates the contribution of each modality, emphasizing dominant sources while efficiently enhancing the representational capability of feature maps. Finally, we introduce Adaptive Adversarial Cross-Entropy Meta-learning (AACEM) for model pre-training. By combining meta-learning with sharpness-aware minimization and utilizing adaptive adversarial cross-entropy loss, AACEM improves both generalization and overall performance. Experimental results show that CEA-Net achieves an accuracy of 97.40%, outperforming networks such as EfficientViM and D2R by margins of 0.81%, 0.56%, 0.67%, and 0.55% across various metrics, demonstrating its significant practical value in corn disease management. Our code and dataset are available at: https://github.com/yiyuynanodesu/CEA-Net. [ABSTRACT FROM AUTHOR]
ISSN:00313203
DOI:10.1016/j.patcog.2025.112788