Measure every animal, without weigh-bridges, manual scoring, or daily handling
Camera-based phenotype measurement for livestock: weight estimation, body-condition scoring, gait monitoring, health flags. Captures individual-animal data at scale without disruptive handling.
Discuss a pilot →Quick facts
- Business size
- Mid-size and enterprise livestock operations, breeding programs, dairy operations
- Timeline
- 6-10 weeks test, 3-5 months pilot, 6-12 months production
- Budget range
- Pilot from €40k. Camera and farm infrastructure separate.
- Hardware
- Fixed cameras at chutes / gates / feed areas, edge compute for on-farm processing.
- Data needed
- Reference measurements (weights, scores) for training. Animal ID tags (RFID / visual).
- Evolution
-
- Genesis
- Custom-built
- Product
- Commodity
New ground. The models exist but are still shaky. We work it out as we go.
What this scale means
Further right means more proven and cheaper. Further left means newer and riskier. Here is the test for each step.
- Commodity
- You could get the result yourself from a ready service, with almost no work. We rarely take these on.
- Product
- A vendor already sells this result turnkey, like shelf recognition from Trax or document reading from ABBYY. If one of them fits you, use it. You come to us when it does not: when it has to run on your own servers, cost less, or fit systems the product cannot reach.
- Custom-built
- Vendors sell only the parts. A tool like Tableau hands you charts, but the dashboards and metrics for your business still have to be built. That build is the work, and that is us.
- Genesis
- The approach exists but does not work reliably yet. You are betting on it maturing, so it costs more and carries more risk.
Expected outcomes
The Problem
Individual-animal data drives modern livestock operations: weight gain per animal informs feed strategy, body-condition scoring informs health intervention, individual milk yield drives breeding decisions. Getting that data conventionally requires handling, weigh-bridges, manual scoring by experts, palpation.
The problem: handling is expensive, stressful for animals (which affects performance), and rarely happens often enough. Weigh-bridge throughput limits how often you weigh. Expert body-condition scorers cost money and aren’t available daily. The data exists in theory; in practice, decisions are made on stale or sample-based information.
Camera-based phenotyping changes the data availability. Cameras at chutes, gates, feed areas continuously measure every animal, weight estimation, body-condition scoring, gait, behavior, without handling. Individual-animal data becomes daily-or-better.
What the Solution Does
A computer-vision system for livestock measurement.
- Camera infrastructure, fixed cameras at high-traffic points (chutes, gates, feed areas).
- Animal identification, link camera observations to individual animals (RFID, visual identification, or both).
- Measurement, weight estimation, body-condition scoring, gait analysis, behavior tracking.
- Aggregation, per-animal time-series of measurements.
- Alerts and insights, health flags, feed-strategy recommendations, breeding-decision support.
- Continuous improvement, model gets better with more data; new species / breeds added via retraining.
Where It Fits
This makes sense if you…
- Operate at scale where individual-animal data drives meaningful decisions
- Have the farm infrastructure (chutes, gates, feed areas) where cameras can be installed
- Use animal IDs (RFID or visual)
- See real cost from infrequent weighing / handling / scoring
This is probably not the right time if you…
- Operate at small scale where manual observation suffices
- Lack the farm infrastructure for fixed cameras
- Need contact measurements (carcass scoring, certain veterinary measurements), CV is non-contact only
Business Value
Frequency uplift. Weights and scores go from sample-based (monthly handling) to continuous (every time the animal passes a camera).
Reduced handling. Less stress on animals, less labor on the farm, lower veterinary risk.
Early health detection. Subtle changes in gait, body condition, behavior surface health issues days earlier than visual observation by busy farmhands.
Better breeding decisions. Individual-animal performance data with daily granularity informs breeding strategy with much higher confidence.
How It Works
1. Camera infrastructure
Cameras mounted at chutes (single-animal passage with good angles), gates (entry/exit traffic), feed areas (extended observation). RGB, sometimes RGB-D (depth) for accurate weight estimation.
2. Animal identification
RFID tags and visual identification. The system matches camera observations to animal IDs from your farm management system.
3. Measurement models
Per-species models:
- Cattle: weight estimation from 3D shape, body-condition scoring, gait analysis.
- Dairy: weight, body condition, plus milk-yield correlation and body-frame scoring.
- Pigs / sheep: weight estimation, body-condition, behavior monitoring.
YOLO-family for detection, ResNet / EfficientNet for classification, 3D reconstruction for weight estimation (similar to the cargo-inspection dimensional measurement work).
4. Aggregation
Each animal accumulates a time-series. Per-day weight, per-week body-condition, per-month behavior summary.
5. Farm management integration
Output flows to the farm management software (vendor varies, we integrate per client).
6. Continuous improvement
Datapipe handles incremental retraining. New breeds, new conditions, new measurements added without rebuilding.
Stack
YOLO-family for detection, custom 3D / shape models for measurement, Datapipe, Jetson-class edge compute on-farm, integration with farm management software.
What You Need to Make This Work
Data. Reference measurements (calibration scale weights, expert body-condition scores) for initial training.
Integrations. Animal ID system (RFID and visual). Farm management software.
Hardware. Cameras (we spec), edge compute, network connectivity.
Team. Farm operations lead. Veterinarian for validation. IT contact for integration.
Implementation Roadmap
1. Test (6-10 weeks)
One camera point, one species, calibration data collection. Build first model, measure accuracy vs ground truth. Output: working measurement at one point.
2. Pilot (3-5 months)
Multiple camera points across one farm. Wire up animal-ID integration. Build alerts and farm-management dashboards. Output: working farm-scale deployment.
3. Production (6-12 months)
Multi-farm rollout. Continuous retraining. Specialty models per breed / species as needed.
Keep in Mind
- Calibration data is critical. Without scale-based reference weights, weight-estimation models can’t be trained.
- Per-breed / per-species tuning is real work. A model trained on Holstein dairy cattle doesn’t generalize to beef breeds without retraining.
- Camera positioning matters. Animals at unusual angles or partially occluded produce lower-confidence measurements.
- Edge compute is essential. Farms often have unreliable internet; on-prem processing is required.
- Integration with farm-management software varies. Each vendor has its own quirks; we adapt.
- Veterinarian sign-off matters. Body-condition scoring is a veterinary measurement; experts should validate model outputs during deployment.
FAQ
Which species do you support?
Cattle (dairy and beef), pigs, sheep are most common. Other species (poultry, goats, horses) doable with appropriate training data.
Can this work with existing CCTV?
Sometimes, depending on camera angles and resolution. New cameras at chutes and gates usually deliver better data.
How accurate is weight estimation?
±3-8% vs scale on cattle (well-calibrated). Better for animals at stable body condition; worse for very lean or very heavy outliers.
Integration with breeding software?
Yes, major breeding-management platforms integrate via API. We adapt to non-standard ones.
Ready to Discuss?
If you operate livestock at scale and individual-animal data is a real decision input, this is a worthwhile pilot. We’ll walk through your farm setup and your management software, and tell you what to expect.
Part of: Computer Vision ↗