Scoping review of precision technologies for cattle monitoring
Bibliographic record
Abstract
• Precision livestock farming is growing to meet global demand for cattle products. • Machine learning is increasingly popular, mainly for computer vision applications. • Many studies determine animal activity or health, suggesting need for specificity. Livestock farming has increased in complexity considerably due to the growing demand for animal products combined with a decreasing number of farmers and ranchers. To meet this challenge, Precision Livestock Farming (PLF) aims to develop fully automated tools to continuously monitor animals, such as cattle, to detect issues earlier and improve productivity. The objective of this scoping review is to provide an overview of precision livestock farming technologies used for cattle monitoring. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) guidelines were followed in this review. Peer-reviewed journal and conference papers from 2005 to 2023 were included, with a focus on technological systems used for cattle monitoring or disease detection. Extracted data included publication year, geographical region, type of technology used, type of monitoring, goal of the intervention, and the level of validation. The relationships between the technology, type of monitoring, and goal of intervention were also explored. 413 papers were found to meet the eligibility criteria. The countries with the most papers were China ( n = 55), Japan ( n = 52), the United States ( n = 38), Australia ( n = 25), and India ( n = 20). The most common types of technology were found to be inertial sensors (37 %) and images or videos (35 %). Simple classification methods were used in 48 % of papers and machine learning in 29 %. The two most common goals stated in PLF papers were determining animal behavior (30 %) and animal health (12 %). Overall, the results provide a snapshot of the types and uses of technologies in PLF for cattle management and suggest emerging technologies and applications of these tools to improve cattle health and welfare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.162 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".