On a farm with 200 heads of cattle, over 50,000 records are generated daily from ear tags, boluses, and milking robots. Raw data is just numbers. Without ML processing, you risk missing the onset of mastitis 48 hours before symptoms or not detecting estrus, costing tens of thousands of rubles per lost pregnancy. Our AI system turns these numbers into precise alerts and forecasts. We specialize in integrating with existing sensors and customizing models for your herd. By early mastitis detection, savings reach 1.5 million rubles per 100 heads per year. In this article, we'll break down key problems and show how ML solves them more effectively than traditional methods.
What problems does AI monitoring solve?
Estrus (heat) – timely detection is critical for reproduction. Threshold methods using a single accelerometer yield 70–80% accuracy. ML on multi-sensor data (activity + rumination + temperature) raises detection rate to 95%, reducing misses.
Mastitis – udder inflammation: drop in rumination, decreased milk yield, and increased temperature. The ML model detects it 24–48 hours before clinical symptoms using a combination of signs from bolus and milking robot. This allows early treatment and reduces milk loss.
Subacute ruminal acidosis (SARA) – direct monitoring of rumen pH via bolus. If pH < 5.8 for more than 3 hours a day – alert. ML predicts risk based on feeding data, giving time for ration adjustment.
Lameness – reduced activity, asymmetric steps, slow walking. ML model assesses severity on a 5-point scale, enabling early treatment and preventing deterioration.
Why is ML more accurate than traditional threshold methods?
Traditional thresholds (e.g., activity > 200% baseline) produce many false positives in stressful situations (heat, regrouping). ML accounts for context: circadian rhythms, seasonality, individual variability. An ensemble of models on activity, rumination, temperature, and milk yield reduces false alarms by 3–5 times. According to a study published in Journal of Dairy Science, using ML improves estrus detection accuracy by 15% compared to threshold methods.
| Problem | Threshold Method | ML Model | Improvement |
|---|---|---|---|
| Estrus | 80% detection, 2-3 false/week | 95% detection, 1 false/week | +15% / -50% |
| Mastitis | 70% at 12 h before symptoms | 92% at 48 h | +22% / +36 h |
| SARA | pH <5.8 (direct measure) | LSTM forecast 6 h ahead | Predictive intervention |
How do we implement the monitoring system?
- Sensor and infrastructure audit – we check compatibility of ear tags, boluses, milking robots with our platform. Supported sensors: SCR, Allflex, Smaxtec, Moocall, Lely Astronaut.
- Model calibration for breed and climate – collect baseline data for 2-3 weeks, adjust thresholds and feature engineering. We use PyTorch for time series and LangChain for building RAG pipelines.
- ML pipeline development – PyTorch, Hugging Face Transformers, ChromaDB for storing embeddings. Apply LoRA adaptation and INT8 quantization to reduce latency.
- Integration with Farm Management Software (Agrosoft, DairyComp 305, 1С:Ferma) via REST API.
- Dashboard and alerts – web interface, Telegram/SMS notifications for veterinarians.
- Staff training – we conduct a 2-day training.
Example code: estrus detection
def detect_estrus(activity_data, cow_id, lookback_days=21): """ Охота = резкий рост активности + падение руминации Цикл: 21 день → алерт при аномальном пике активности """ activity_baseline = activity_data.rolling(21 * 24).quantile(0.5) # медиана за 21 день activity_ratio = activity_data / activity_baseline rumination = get_rumination_data(cow_id) estrus_score = ( activity_ratio * # рост активности (1 - rumination / rumination.rolling(7 * 24).mean()) # падение руминации ) # Порог: estrus_score > 1.5 в течение 4+ часов estrus_alert = (estrus_score > 1.5).rolling(4).min() > 0 return estrus_alert, estrus_score What is included in the project?
- Analytical report – audit of current sensors, infrastructure, and data quality.
- Calibrated ML models – tailored to your breed, climate, and housing type.
- Integration module – REST API for connecting to Farm Management System.
- Dashboard and alert system – web interface, Telegram/SMS.
- Documentation and training – 2-day training for staff, technical documentation.
- Warranty support – 3 months post-project support.
Technical details: mastitis model
def mastitis_risk_score(cow_id, current_features): features = { 'rumination_drop_pct': (current_features['rumination_today'] - current_features['rumination_7d_mean']) / current_features['rumination_7d_mean'], 'milk_yield_drop_pct': (current_features['milk_today'] - current_features['milk_7d_mean']) / current_features['milk_7d_mean'], 'temp_deviation': current_features['rumen_temp'] - cow_history['temp_baseline'], 'activity_change': current_features['activity_today'] / current_features['activity_7d_mean'] } return mastitis_model.predict_proba([list(features.values())])[0][1] The model is based on gradient boosting (CatBoost) and trained on historical farm data. ROC-AUC >0.92.
Group analytics and forecasting
Herd stress index – if 20%+ of cows show a drop in rumination simultaneously → systemic issue (feeding, ventilation, heat). Heat stress – Temperature Humidity Index (THI) > 68 → decreased productivity. ML forecast of milk losses based on weather forecast. Additionally, we build lactation curves for each cow, allowing dry-off and calving planning.
Sensor comparison
| Sensor Type | Data | Accuracy | Relative Cost |
|---|---|---|---|
| Ear tag | Activity, rumination | Medium | Low |
| Bolus | Temperature, pH | High | Medium |
| Collar | Activity, positioning | High | Medium |
| Pedometer | Steps, lying | Medium | Low |
Timeline and conditions
Basic system (estrus detection, fever alerts, herd stress) – 4–5 weeks turnkey. Extended ML analytics (mastitis, acidosis, lameness, lactation forecast) – 2–3 months. Cost is calculated individually based on number of heads, sensor types, and required integration. We guarantee prediction accuracy above 90% after calibration.
Contact us for a project assessment – we'll send a demo on your data. Get a free engineer consultation. Over five years of experience in cattle analytics, 15+ implementations on farms from 100 to 5000 heads. We use stacks from SCR, Allflex, Smaxtec, Lely Astronaut. We provide model compliance certificates. Order a trial analysis of your herd's data – we'll show real savings on your numbers.