Plainsight Vision AI for Livestock

Overview

Plainsight Vision AI is an enterprise computer vision platform that brings precision livestock counting and health monitoring to large-scale cattle operations. Partnering with JBS (one of the world's largest food producers) and powered by NVIDIA Metropolis technology, Plainsight achieves 99.5%+ accuracy in cattle counting, approximately 2 percentage points better than manual human counting.

The platform uses edge AI deployed on NVIDIA GPUs to process multiple video streams in real time, detecting, tracking, and counting cattle as they move past fixed-position cameras. Beyond counting, the system monitors individual animal gait, size, and behavior patterns for early health issue detection.

Snapshot

Description: Enterprise computer vision platform using NVIDIA-powered edge AI for precision livestock counting (99.5%+ accuracy), health monitoring, and inventory management.

Who It's For: Large-scale cattle operations, feedlots, packing facilities, and enterprises requiring accurate livestock inventory and health monitoring.

What It Does Best: Achieves 99.5%+ counting accuracy, eliminating costly inventory discrepancies while enabling automated health monitoring through gait and behavior analysis.

Why It Matters: Counting inaccuracies cost the livestock industry hundreds of millions of dollars annually. At $980-1,200 per head, even small percentage improvements in accuracy deliver significant ROI.

Ideal Users

Farm Types: Large feedlots, packing facilities, receiving operations, commercial cattle operations

Herd Size: Enterprise scale (5,000+ head) where counting accuracy directly impacts financial outcomes

Roles: Operations managers, inventory specialists, health monitoring staff, IT/technology teams

Main Features

Precision Cattle Counting

99.5%+ accuracy through object detection, tracking, and registration/deregistration zone processing

Real-Time Multi-Stream Processing

Processes multiple video streams per GPU simultaneously for continuous monitoring

Individual Animal Tracking

Monitors gait, size, and behavior patterns; facial recognition for identifying specific animals

Health Monitoring

Detects symptoms like abnormal gait and behavior for early disease detection

Edge AI Deployment

Secured private network deployment via NVIDIA Jetson for low-latency processing

Directional Awareness

Prevents double-counting through intelligent tracking and counting line logic

Advanced Features

Automated Anomaly Alerts

Real-time notifications when health or behavior anomalies are detected

Centralized Data Repository

Visual data management from pastureland with trend prediction and tracking

Blockchain Integration

CattlePass NFT project for livestock traceability and supply chain tracking

Weight Tracking

Population-level weight monitoring across herds

Data and Integrations

Integrates With: Existing camera infrastructure, enterprise management systems, NVIDIA Metropolis platform

Data Flows: Real-time counting data to inventory systems; health monitoring alerts to management; visual data archived for analysis

Implementation

Timeline: 8-week deployment timeline (faster than expected months)

Infrastructure: Fixed-position cameras at key livestock movement points, NVIDIA GPU hardware, secured private network connectivity

Installation: Professional deployment by Plainsight integration team

Tips

Position cameras at natural cattle movement points (receiving, transfer lanes)

Establish counting baselines to measure accuracy improvement

Integrate with inventory management systems for real-time reconciliation

Define alert thresholds for health monitoring notifications

Training and Support

Training: Platform navigation, alert configuration, integration setup, minimal learning curve

Support: Enterprise support, ongoing software updates, model recalibration

Pricing

Model: Enterprise subscription (contact for pricing)

ROI: Facilities processing 1,000-5,000 cows per day at $980-1,200 per cow. Even 0.5% accuracy improvement translates to significant savings.

Pros and Limitations

Strengths

99.5%+ counting accuracy (better than manual)

Real-time processing on edge devices

Combined counting and health monitoring

Partnership with major industry players (JBS, NVIDIA)

8-week deployment timeline

Directional awareness prevents double-counting

Blockchain-ready for traceability

Limitations

Enterprise-focused (not suitable for small operations)

Requires significant infrastructure investment

Primary focus on feedlot/processing operations

Specialized hardware requirements (NVIDIA GPUs)

Best For

Large feedlots and packing facilities

Operations with significant inventory management costs

Enterprises requiring supply chain traceability

Operations processing 1,000+ head daily

Not Ideal For

Small dairy or cattle operations

Farms without IT infrastructure

Operations without inventory accuracy concerns

Budget-constrained implementations

Evaluation Questions

What is our current livestock counting accuracy and associated costs?

How many head do we process daily and at what value per head?

What is our existing camera and network infrastructure?

Do we need supply chain traceability or blockchain integration?

What are our health monitoring priorities and current methods?