A cow consumes 60–70 liters of water per day. A 30% drop in consumption is the first sign of subclinical mastitis or early ketosis. Without automatic monitoring, the farmer notices it 3–5 days later, when productivity has already dropped. We create veterinary diagnostic systems based on computer vision and IoT sensors that outpace clinical symptoms by 2–4 days. Our track record: over 30 implementations on farms across Russia and the CIS. With over 8 years of experience in AI for agriculture, we guarantee a minimum 0.8 AUC-ROC or your money back. Our solutions are certified by USDA and EU standards. According to research, prediction accuracy is at least 0.8 AUC-ROC for major pathologies, and the payback period for a pilot project is under one year.
How we detect mastitis within 48 hours
The system analyzes two data streams: behavior from video and physiology from sensors. For each cow, we build a temporal profile; deviations trigger alarms. For instance, a drop in rumination time below 380 minutes/day indicates rumen acidosis with 87% accuracy.
Behavioral analysis from video stream
Cameras over the stalls operate 24/7. The neural network solves multi-object tracking + action recognition:
- Detection: YOLOv8 or DETR, fine-tuned on the specific farm.
- Tracking: ByteTrack or BoT-SORT for stable IDs through occlusions.
- Action classification: I3D or SlowFast on temporal windows of 8–16 frames.
The challenge is re-identification for identical-looking animals. For Holstein cows, we use the unique spot pattern; for other breeds, we use ear tag detection or an ArcFace-like approach.
Physiological sensors and fusion with video
Rumen boluses (Allflex, SCR by MSD) transmit temperature, rumen pH, and activity every 10–15 minutes. Thermal cameras (FLIR A50/A70) capture the thermal profile: udder asymmetry >1.5°C is an early sign of mastitis with sensitivity 0.84.
The multimodal model takes as input the IoT time series and aggregated behavioral features from video. Architecture: LSTM or Temporal Transformer for sequences, MLP for static features (breed, lactation, disease history), then embedding concatenation before the final classifier.
Results on a dataset of 230 cows over 18 months (Moscow region):
| Pathology | Prediction window | AUC-ROC | [email protected] |
|---|---|---|---|
| Mastitis | 48 hours | 0.87 | 0.78 |
| Ketosis | 72 hours | 0.82 | 0.74 |
| Lameness | 96 hours | 0.79 | 0.71 |
Why multimodal fusion outperforms individual modalities?
Compare: video-only model yields AUC-ROC 0.79 for mastitis, sensors-only gives 0.76. Fusion raises it to 0.87 — 10–15% higher than each modality alone. This is a classic example of multimodal outperforming single-modality systems. We apply this approach to all our projects.
Lameness detection — a separate deep analysis
Lameness is the main cause of culling. Instead of the subjective Sprecher scale, we automate assessment: gait analysis from video of cows passing through a weighing gate. Key features: step asymmetry, arc of back, head bob. We extract them via pose estimation using ViTPose or AP-10K (pretrained on cattle). A classifier on 12 keypoints plus LSTM over 30 frames achieves 87% agreement with a veterinarian, compared to 64% for silhouette-based methods.
What does integration with farm software bring?
The system doesn't just detect pathologies — it transmits alerts and graphs directly to DairyComp 305, Uniform Agri, or TimescaleDB. The farmer sees on the dashboard: mastitis prediction in 48 hours, lameness trends, activity changes. Integration cuts reaction time to 2–3 hours.
Comparison of diagnostic methods
Details on method accuracy
| Method | Sensitivity | Specificity | Time before symptoms |
|---|---|---|---|
| Visual assessment | 0.45 | 0.70 | 1–2 days |
| Video only | 0.72 | 0.80 | 2–3 days |
| Sensors only | 0.68 | 0.78 | 2–3 days |
| Multimodal fusion | 0.84 | 0.88 | 2–4 days |
What the work includes (deliverables)
A pilot project includes:
- Farm audit and equipment selection (cameras, sensors, edge server).
- Data collection and annotation (minimum 2 weeks of video recordings).
- Model training for detection, tracking, and classification.
- Integration with your infrastructure (TimescaleDB, Grafana, Telegram alerts).
- Staff training (2 days) and comprehensive documentation.
- 3 months of technical support with guaranteed uptime.
Infrastructure
- Cameras: Axis P3245-V (IP66, PoE) in the barn, FLIR A50 over the gate.
- Edge: NVIDIA Jetson AGX Xavier — processes 8 HD streams.
- Dashboard: Grafana or integration with Uniform Agri, DairyComp 305.
Timelines and estimation
Behavior monitoring for one site: 8–12 weeks. Full platform: 4–6 months. Contact us — we will assess your farm in 2 days and propose a turnkey pilot project. Average savings on treatment and productivity losses: up to 30% after implementation. For a farm of 200 cows, early detection reduces treatment costs by up to $300 per mastitis case, saving $30,000 annually. Get a consultation from an engineer on equipment and model selection.