AgriLM: Cross Modal Visual Language Reasoning with Multi Vector Retrieval for Multimodal Knowledge Analytics
Abstract
This repository presents AgriLM, a unified multimodal framework for cross-modal visual-language reasoning in precision agriculture. The architecture combines CLIP-based vision encoding with a domain-adapted large language model via a cross-modal transformer, enabling token-level interaction across modalities. A multi-vector representation strategy preserves entity-level semantics, improving fine-grained retrieval using FAISS-based approximate nearest neighbor indexing. The system incorporates retrieval-augmented generation (RAG) to ensure evidence-grounded and interpretable outputs. Evaluations show improved cross-modal alignment, retrieval precision, and diagnostic accuracy (up to 92.8%) on real-world agricultural datasets.
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