From pasture to warehouse, counted and measured

A camera above the conveyor belt measures, frame by frame, how much of the belt width is covered by grain (soybeans, corn, wheat, coffee), meal, ore, crushed stone or sand. The system shows the load in real time and over time, identifies the belt running empty (energy and wear with no production), overload with a risk of spillover, and off-center load that causes mistracking and spillage — and combines load and speed into a flow index to balance the feed.

How it works

1. 1The user marks the belt section with 4 corners; the image is rectified (perspective removed) and split into transverse scan lines.
2. 2Text-prompted segmentation (CLIPSeg) separates material from empty belt, without training; a color and texture model fitted on the frame itself refines the load edge.
3. 3On each scan line, the fraction of the width covered is measured; the average is the load, and the load centroid indicates centering.
4. 4Apparent speed is measured by phase correlation along the flow, discounting the movement of the adjacent structure (compensates for camera movement).
5. 5Configurable bands (empty, low, optimal, overload) trigger alerts with photos: empty belt, overload/spillover and off-center load.

Benefits

• Eliminates hours of belts running empty — direct savings on energy and on belt and idler wear.
• Prevents overload and spillover, which cause product loss, cleanup and downtime.
• Detects off-center load before belt mistracking.
• Provides continuous visibility of the feed to balance hoppers, dryers and crushers.

Automatic animal counting with fixed cameras or drones: cattle passing through a gate, chute or alley (virtual line), herds on pasture or in feedlots seen from above, sheep and goats in pens and poultry in barns. The AI identifies each animal's species, separates herding dogs and people, estimates herd size and measures area occupancy — replacing slow, error-prone manual "head counts".

How it works

1. 1Text-prompted detection (OWLv2): the AI looks for "a cow", "a sheep", "a goat", "a horse", "a pig" or "a chicken" in each frame, without specific training — it works with any breed and in drone views.
2. 2For ground cameras there is a fast mode (YOLO11 trained on COCO) that analyzes more frames per second.
3. 3Dense herds or overhead footage: the frame is sliced into overlapping tiles (SAHI technique) and each tile is analyzed at higher resolution; detections are merged without duplicating animals.
4. 4Each animal gets a track (ByteTrack); when it crosses the virtual line at the gate or chute it is counted only once, by species.
5. 5Estimated herd = highest stable count in the frame (median of 5 frames), useful for pasture and feedlot flyovers.
6. 6Herding dogs and people are marked but not included in the count.
7. 7Occupancy = share of the image covered by animals; with a stocking limit configured, it raises an alert with a snapshot of the moment.

Benefits

• Eliminates manual "head counts", which require stopping handling and go wrong on large lots.
• Counting by species and direction of passage, with video recording as evidence.
• A drone flyover becomes a herd inventory in minutes, without rounding animals up in the corral.
• Text-prompted detection enables new species without training a model.

Automatic animal counting with fixed cameras or drones: cattle passing through a gate, chute or alley (virtual line), herds on pasture or in feedlots seen from above, sheep and goats in pens and poultry in barns. The AI identifies each animal's species, separates herding dogs and people, estimates herd size and measures area occupancy — replacing slow, error-prone manual "head counts".

How it works

1. 1Text-prompted detection (OWLv2): the AI looks for "a cow", "a sheep", "a goat", "a horse", "a pig" or "a chicken" in each frame, without specific training — it works with any breed and in drone views.
2. 2For ground cameras there is a fast mode (YOLO11 trained on COCO) that analyzes more frames per second.
3. 3Dense herds or overhead footage: the frame is sliced into overlapping tiles (SAHI technique) and each tile is analyzed at higher resolution; detections are merged without duplicating animals.
4. 4Each animal gets a track (ByteTrack); when it crosses the virtual line at the gate or chute it is counted only once, by species.
5. 5Estimated herd = highest stable count in the frame (median of 5 frames), useful for pasture and feedlot flyovers.
6. 6Herding dogs and people are marked but not included in the count.
7. 7Occupancy = share of the image covered by animals; with a stocking limit configured, it raises an alert with a snapshot of the moment.

Benefits

• Eliminates manual "head counts", which require stopping handling and go wrong on large lots.
• Counting by species and direction of passage, with video recording as evidence.
• A drone flyover becomes a herd inventory in minutes, without rounding animals up in the corral.
• Text-prompted detection enables new species without training a model.