AI Monitoring Modules Built for Poultry Farms

Tracks shed temperature against age-based comfort bands for the flock.

Watches relative humidity to prevent wet litter and heat stress.

Detects rising NH₃ from litter before it harms respiratory health.

Tracks carbon dioxide to confirm adequate air exchange in the house.

Measures particulate and dust load affecting bird and worker health.

Verifies lighting intensity and photoperiod match the welfare program.

Learns normal shed conditions and flags subtle multi-sensor deviations.

Screens birds via thermal imaging to flag elevated body temperature.

CNN classifies dropping images for early signs of enteric disease.

Listens for coughing and rales to catch respiratory outbreaks early (roadmap).

Detects downed or deceased birds from video for prompt removal.

Estimates average bird weight and uniformity from camera and scale data.

Tracks feed consumption to spot appetite drops and wastage.

Monitors per-line water use as an early indicator of flock health.

Computes feed conversion ratio to benchmark efficiency over the cycle.

Tracks lay rate and egg counts for layer flocks against expected curves.

Measures flock activity levels from video to gauge welfare and comfort.

Detects bird clustering that signals heat stress or ventilation gaps.

Coordinates module outputs, deduplicates, and routes alerts to the right people.

Compiles daily and cycle-level flock-health and production reports.