Animal detection & counting
Challenge
Herd members overlap and appear at different scales.
Our approach
Label animal instances and record visibility to support consistent counts.
Introducing advanced AI data pipelines for Livestock
Consistent animal data for herd-monitoring research
Barn cameras, pasture footage and animal movement sequences need labels that capture real operational detail. Engai prepares livestock datasets around your objects, scenes and review requirements.
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Livestock Data Annotation
Expert Labeling
of Livestock Data
Livestock Data Annotation services for a wide range of computer vision applications.
0
completed projects
Smart Farm AI
Livestock · Multi-Target Bounding Box Detection
0
annotated files
Pose & Bounding Precision
New “smart farm” technologies are monitoring the health of farm animals with a high degree of accuracy, using cameras and artificial intelligence. Detailed observation by AI-powered image analysis could enable early detection of injuries and illnesses that may impact the quantity and quality of your production.
Accurate labeling of your livestock data can help train AI for many useful tasks. Most common types of annotation:
Livestock / Capabilities
Livestock Core Solutions
Carefully annotated data for livestock perception, automation and research.
Livestock label taxonomy
Define classes for animals, visible joints and herd instances, with visual examples and explicit boundary rules.
Capture-aware quality review
Review the effects of lighting, scale and occlusion in barn cameras, pasture footage and animal movement sequences.
Model-ready annotation data
Connect livestock labels to source identifiers, scene metadata and the export schema your model uses.
Annotation capabilities
Livestock Data Services
Barn cameras, pasture footage and animal movement sequences need labels that capture real operational detail. Engai prepares livestock datasets around your objects, scenes and review requirements.
Locate each visible object with an axis-aligned rectangle and a class label. Applied to animals, visible joints and herd instances.
Data Annotation for Livestock
AI livestock monitoring
Label animals and observable activities across barn and pasture imagery with consistent visibility rules.
Animal movement analysis
Mark visible joints and gait phases to support movement research and specialist review.
Feeding behavior datasets
Annotate feeding intervals and animal positions around feeding areas without inferring unseen intake.
Computer vision herd counting
Separate crowded animal instances and record partial visibility for robust count annotations.
Livestock workflow assistants
Organize husbandry records and reviewed task examples for herd-management information tools.
Client reviews
on 
Careful work on the small details
The sample batch helped us agree on how to handle overlapping leaves and partially visible crops.
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Having those edge cases discussed early would give our team a clearer baseline before scaling annotation.
Clear communication throughout
Our labeling requirements evolved as we reviewed the imagery. The feedback process felt straightforward.
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A shared set of examples and regular checkpoints would help keep the dataset consistent across batches.
A practical approach to field data
Field images are rarely perfect. We appreciated the attention to shadows, occlusion and changes in lighting.
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These are the details we would want a labeling partner to consider when preparing data for crop-monitoring models.
Livestock in practice
Annotation applications across livestock workflows.
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Animal detection & counting
THE CHALLENGE
Herd members overlap and appear at different scales.
THE SOLUTION
Label animal instances and record visibility to support consistent counts.
DATA DELIVERABLE
Reviewed bounding boxes data with a consistent label schema and documented edge cases.
Animal detection & counting
+15%
Throughput target
A project target for improving throughput with task-specific training data.
Sub-CM
Resolution target
Where calibrated source imagery supports fine spatial measurements.
40%
Efficiency target
A project target for reducing review effort through assisted labeling.
Deployment options
Build your workflow around the infrastructure your team uses.
On-Premises
Secure local processing for sensitive datasets.
Cloud
Highly scalable infrastructure powered by AWS/GCP.
Edge
Real-time inference optimized for on-device hardware.
Formats & platforms
ONNX
TensorRT
Core ML
OpenVINO
LiteRT
TorchScript
PaddlePaddle
IMX500
Qualcomm
Huawei
Available formats depend on your model, runtime and target hardware.
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Live Infrastructure Telemetry
1.2BData Points
189Models
24Pipelines
3Alerts
TypeOriginOwnerStatus
Vision ModelGitHubDave DigsterHealthy
DatasetAWS S3Emily ThompsonSyncing
DeploymentGCPRobert BrownActive
Zero-Trust Dataset Confidentiality
Your requirements and proprietary sample assets are encrypted in transit and never used for public model training.