Agriculture

Test the edge cases too dangerous to stage.

You can't safely test autonomous heavy equipment around humans and animals in the real world. Empower your robotics teams to master unpredictable environments, from blinding glare and dense canopies to obscured workers and wildlife, before the machine ever hits the dirt.

Software augmented testing for agricultural autonomy.

Real-fidelity neural reconstructions and deterministic sensor simulation to build, test, and validate perception systems for autonomous agricultural equipment across every season and condition.

Challenges with Agricultural Perception Development

Safe, Reliable Perception

Edge Cases — Unexpected obstacles (wildlife, irrigation equipment, workers) in unstructured outdoor environments are critical but hard to capture.

Observability — Model changes can behave differently across crop types, growth stages, and terrain.

Safety — Equipment operating near workers and livestock carries significant injury risk.

Solution — Continuously evaluate perception across crop stages, seasons, weather, and obstacle scenarios in a virtual environment.

Time and Cost

Seasonal data — The harvest calendar dictates your release cycle. Missing a specific growth stage means waiting a year to validate row-following and navigation algorithms.

Physical Regression Testing — Deploying heavy machinery to physically test new autonomy builds across diverse regions is slow and prohibitively expensive.

Complex Labeling — Manually annotating 3D bounding boxes for drivable surfaces, dense crop canopies, and obscured obstacles is slow and error-prone.

Solution — Generate the scenarios, datasets, and test suites you need across any season, crop, or terrain in days.

Scalability Across Crops and Geographies

Unstructured Terrain — Expanding to new regions means adapting your autonomy software to vastly different soils, topography, and unpredictable farm layouts.

Platform & Hardware Iteration — Upgrading to a new sensor suite or changing equipment platforms (e.g., from a tractor to a sprayer) invalidates existing datasets.

Navigational Hazards — Different crop types require entirely new data campaigns to train for specific hazards, from narrow vineyard rows to broad cornfields.

Solution — Validate terrain handling, test new sensor configurations, and scale to new geographies by updating code, not recapturing data.

PD Solutions

PD Replica + PD Sim for agricultural autonomy across every season.

Evaluate

Open-loop and closed-loop testing integration for agricultural autonomy stacks. Regression testing for obstacle detection, row following, and implement control. Perception unit testing across crop stages, weather, lighting, and terrain variability. Near-validation testing in real-world scanned fields and farms (PD Replica).

Analyze

Optimize hardware and prove out safety cases before building physical prototypes. Benchmark autonomy performance against high-risk field scenarios and near-miss events. Analyze how different sensor configurations (camera, lidar, radar) handle dense canopy penetration, heavy dust clouds, and obscured obstacles in completely unstructured environments.

Train

Defeat the seasonal data bottleneck with perfectly labeled, multi-sensor data generated on demand. Train models at scale to detect drivable surfaces, track severe terrain variations, and identify obscured workers or wildlife. Generate high-fidelity datasets across infinite variations of canopy densities, seasons, and lighting conditions to close the sim-to-real gap.

Benefits

Addressing Industry Challenges

Break free from seasonal constraints

Don't let the harvest calendar dictate your engineering velocity. Simulate diverse crop stages, changing canopy densities, and extreme weather instantly. Compress years of off-road data collection into days of simulation to continuously test your autonomy stack.

Scale across environments & platforms

Adapt your autonomy stack to vastly different topographies, terrain types, and unpredictable farm layouts through software configuration. Validate new sensor payloads or switch equipment platforms without launching massive, expensive new physical data campaigns.

Operate safely around workers and livestock

Operating multi-ton autonomous machinery off-road carries massive physical risk. Exhaustively test your perception system against high-stakes near-misses, obscured workers, and sudden wildlife—scenarios too dangerous or impossible to ever stage in the real world.

Schedule a Demo

See how Parallel Domain can accelerate your agricultural autonomy development. Fill out the form and our team will be in touch.