Cross-Modal Data Fusion via Vision-Language Model for Crop Disease Recognition Article Swipe
Wenjie Liu
,
WU Guo-qing
,
Han Wang
,
Fuji Ren
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25134096
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25134096
Crop diseases pose a significant threat to agricultural productivity and global food security. Timely and accurate disease identification is crucial for improving crop yield and quality. While most existing deep learning-based methods focus primarily on image datasets for disease recognition, they often overlook the complementary role of textual features in enhancing visual understanding. To address this problem, we proposed a cross-modal data fusion via a vision-language model for crop disease recognition. Our approach leverages the Zhipu.ai multi-model to generate comprehensive textual descriptions of crop leaf diseases, including global description, local lesion description, and color-texture description. These descriptions are encoded into feature vectors, while an image encoder extracts image features. A cross-attention mechanism then iteratively fuses multimodal features across multiple layers, and a classification prediction module generates classification probabilities. Extensive experiments on the Soybean Disease, AI Challenge 2018, and PlantVillage datasets demonstrate that our method outperforms state-of-the-art image-only approaches with higher accuracy and fewer parameters. Specifically, with only 1.14M model parameters, our model achieves a 98.74%, 87.64% and 99.08% recognition accuracy on the three datasets, respectively. The results highlight the effectiveness of cross-modal learning in leveraging both visual and textual cues for precise and efficient disease recognition, offering a scalable solution for crop disease recognition.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s25134096
- https://www.mdpi.com/1424-8220/25/13/4096/pdf?version=1751295775
- OA Status
- gold
- Cited By
- 2
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411801849
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411801849Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s25134096Digital Object Identifier
- Title
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Cross-Modal Data Fusion via Vision-Language Model for Crop Disease RecognitionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-30Full publication date if available
- Authors
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Wenjie Liu, WU Guo-qing, Han Wang, Fuji RenList of authors in order
- Landing page
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https://doi.org/10.3390/s25134096Publisher landing page
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https://www.mdpi.com/1424-8220/25/13/4096/pdf?version=1751295775Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/25/13/4096/pdf?version=1751295775Direct OA link when available
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Computer science, Artificial intelligence, Machine learning, Feature (linguistics), Pattern recognition (psychology), Modal, Identification (biology), Linguistics, Botany, Chemistry, Polymer chemistry, Biology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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