Traceable Knowledge Services from Long Agricultural Videos: An Evidence-Mapping Review for Cross-Source Recomposition

Long agricultural videos contain time-distributed multimodal evidence, including visual scenes, speech, subtitles, operational context, and expert explanations. Existing work remains fragmented across video perception, retrieval, summarization, monitoring, and knowledge representation, with limited attention to traceable knowledge services. This evidence-mapping review examines how long agricultural videos can be transformed into user-relevant knowledge resources. It proposes a user-driven architecture linking multimodal understanding, knowledge-unit segmentation, temporal retrieval, evidence binding through knowledge graphs and retrieval-augmented generation, and evidence-grounded recomposition. The review also clarifies the operational boundaries among knowledge points, semantic segments, evidence clips, and knowledge units and identifies validation requirements for datasets, benchmarks, expert assessment, user studies and provenance control. A bounded evidence-mapping approach was applied to the Web of Science Core Collection and PubMed, supplemented by citation tracking. Citation tracking included both backward tracking of reference lists and forward tracking of citing articles. The formal search, conducted on 26 June 2026, identified 245 records; after subsequent updates through September 2026, 113 records were retained in the primary coded corpus. Evidence sources were classified as direct agricultural (D), transferable (T), or background evidence (B), whereas conceptual constructs and methodological requirements were labeled P and analyzed separately from empirical evidence. The corpus supports component-level methods, but explicit validation of a complete service chain was not identified in the primary corpus. Remaining challenges concern knowledge-level annotation, query–evidence benchmarking, provenance-aware generation, rights workflows, and user-centered validation. Future studies should test the proposed architecture in bounded agricultural domains with timestamped evidence, agronomic assessment, comparative baselines, and lawful-reuse workflows.