英国农场安全为何需要 AI 门禁监控：一项基于 PRISMA 的乡村犯罪与牲畜捕食证据综述
The Case for AI-Enabled Gate Monitoring as Farm Security in the UK: A PRISMA-Guided Review of the Evidence on Rural Crime, Livestock Predation and Farm Access
AI 导读
一项 PRISMA 系统综述检索 Web of Science 与 Scopus 得到 1,183 条记录，指出英国农场安全证据薄弱：NFU Mutual 估计 2025 年英国乡村犯罪损失达 4,150 万英镑，牲畜盗窃增长近 30% 至约 450 万英镑。
正文 · 原文
Abstract:Farm security in the UK rests on thinner evidence than its cost suggests. We ran a PRISMA guided review: twelve searches of Web of Science and Scopus on 2nd -4th November 2026 returned 1,183 records (761 unique), and grey-literature and hand-searches added 35 assessed sources. We synthesised 96 database records and 24 grey sources narratively. UK peer-reviewed evidence is small (16 records), and the main UK survey of crime and farmer mental health has 80 responses. Cost estimates rest on insurer claims: NFU Mutual put UK rural crime at £41.5 million in 2025, with livestock theft up almost 30% to about £4.5 million and dog attacks on livestock costing £1.95 million. The Home Office states that agricultural crime cannot be separately identified in its central data. We found no independent evaluation of cameras, sensors, trackers or marking; light and sound deterrents lost effect within one to five months in a systematic review of anti-predator interventions; and no study examined how often farm gates are left open or what follows. Camera-trap AI papers report accuracy on ecological datasets, none at a farm gate in the records we found, and in one comparison machine learning detected 41% of wildlife against 81% for analysts. We argue, as an inference from these gaps and not as a demonstrated effect, that continuous gate-level AI-mediated monitoring, as implemented in Fieldmark AI, would be highly beneficial for UK farm security. It would create the farm-level, time-stamped record that no official source holds, verify events that periodic human checks miss, resolve whether foxes, dogs or people were responsible, and give every other security measure something to be tested against. No study has yet shown that it reduces loss, and we set out the evaluation needed to show it.
| Comments: | Keywords: farm security; rural crime; livestock predation; camera traps; computer vision; PRISMA; evidence review |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.13933 [cs.CV] |
| (or arXiv:2611.13933v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13933 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Stephen Symes Dr [view email]
[v1]
Tue, 10 Nov 2026 11:20:39 UTC (769 KB)
来源：arXiv cs.AI · arxiv.org