Multimodal RAG System for Rose Cultivation Using Paragraph-level Chunking and VLM-based Image Captioning

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Key Points

The study aims to enhance the accuracy of RAG systems in agriculture by integrating textual and visual data for rose cultivation.
Developed a multimodal RAG system combining paragraph-level chunking and VLM-based image captioning.
Isolated text and images for extraction and built a vector database using context-integrated captions generated by GPT-4o.
Performance evaluated by comparing Recall@5 and Answer Relevancy metrics against traditional text-based RAG systems.
The proposed system improved Recall@5 by 28.8% compared to traditional methods.
Achieved a 33.3% increase in Answer Relevancy over conventional text-based RAG systems.

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Demonstrates improved performance in rose cultivation, indicating effective integration of textual and visual data.

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Han et al. (2026) studied this question.

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