ForestRAG-AR: An AR and RAG Framework for Context-Aware Silviculture Assistance

Abstract

Silvicultural decision making in forestry relies heavily on technical manuals and expert knowledge that are difficult to access in the field. Meanwhile, advances in wearable augmented reality (AR) and retrieval-augmented generation (RAG) offer new opportunities to deliver context-aware, data-driven guidance directly within natural environments. This paper presents ForestRAG-AR, an integrated AR and RAG framework that provides site-specific silvicultural recommendations grounded in authoritative forestry documents. The system combines (i) a perception and context extraction module that interprets local forest conditions, including tree species, stand density, and terrain slope, through on-device sensing, (ii) a RAG-based knowledge backend that adapts a large language model to forestry by embedding regional silviculture manuals and best-management-practice guides, and (iii) an AR interface that visualizes thinning and pruning guidance with natural-language explanations and verifiable citations. A domain-adapted cross-encoder reranker fuses environmental context with text passages to improve retrieval precision and factual grounding. Experiments on 50 forestry queries demonstrate that ForestRAG-AR improves nDCG@10 by 7.8 points and citation precision to 0.96 while maintaining sub-500 ms end-to-end latency.