ReviewAnimals : an open access journal from MDPI2025
Computer Vision in Dairy Farm Management: A Literature Review of Current Applications and Future Perspectives.
Review in Animals : an open access journal from MDPI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Hooves, Sensors, and Signals: Precision Approaches to Automated Lameness Detection in Dairy Cattle.Animals : an open access journal from MDPI · 2026Review
- DMFRNet: Dynamic Multi-Scale Feature Reweighting Network for Dairy Cow Detection.Animals : an open access journal from MDPI · 2026Article
- Validating Foundation Models for Automated Cattle Detection.Sensors (Basel, Switzerland) · 2026Article
- BoviFusionNet: A Lightweight Edge-Deployable AI System for Cattle Behavior Recognition in Livestock Monitoring.Veterinary sciences · 2026Article
- Computer Vision for Cattle Health and Welfare Monitoring: A Comprehensive Review of Methods, Applications, and Interdisciplinary Integration in Smart Agriculture.Sensors (Basel, Switzerland) · 2026Review
- Recent Advances in Vision-Based Beef Cattle Body Measurement Technologies.Animals : an open access journal from MDPI · 2026Review
- Pose-Driven Cow Behavior Recognition in Complex Barn Environments: A Method Combining Knowledge Distillation and Deployment Optimization.Animals : an open access journal from MDPI · 2026Article
- Multimodal animal health monitoring in extensive livestock production systems.Frontiers in veterinary science · 2026Review
- Video-based cattle behaviour detection for digital twin development in precision dairy systems.npj veterinary sciences · 2026Article
- SideCow-VSS: A Video Semantic Segmentation Dataset and Benchmark for Intelligent Monitoring of Dairy Cows Health in Smart Ranch Environments.Veterinary sciences · 2025Article
- A Novel Lightweight Dairy Cattle Body Condition Scoring Model for Edge Devices Based on Tail Features and Attention Mechanisms.Veterinary sciences · 2025Article
- Integrative assessment of the effects of ventilation systems on economic efficiency, milk production, and reproductive performance in dairy cows.Frontiers in veterinary science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Computer vision is rapidly transforming the field of dairy farm management by enabling automated, non-invasive monitoring of animal health, behavior, and productivity. This review provides a comprehensive overview of recent applications of computer vision in dairy farming management operations, including cattle identification and tracking, and consequently the assessment of feeding and rumination behavior, body condition score, lameness and lying behavior, mastitis and milk yield, and social behavior and oestrus. By synthesizing findings from recent studies, we highlight how computer vision systems contribute to improving animal welfare and enhancing productivity and reproductive performance. The paper also discusses current technological limitations, such as variability in environmental conditions and data integration challenges, as well as opportunities for future development, particularly through the integration of artificial intelligence and machine learning. This review aims to guide researchers and practitioners toward more effective adoption of vision-based technologies in precision livestock farming.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.