Tevel’s Flying Autonomous Robots are not conventional free-flying farm drones. They form an orchard harvesting system that combines tethered robots, computer vision, fruit handling and real-time crop data.
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The quick answer
Tevel’s system uses multiple tethered flying robots on a mobile ground platform. Each robot identifies fruit, estimates qualities such as size and ripeness, picks selected fruit and transfers it to a bin. The value is selective harvesting and labor support; performance still depends on orchard layout, crop variety and deployment economics.
How Tevel’s fruit-picking system works
Tevel calls the aircraft Flying Autonomous Robots. Computer vision detects fruit and foliage, classification estimates traits such as color and ripeness, and path-planning guides the robot into the canopy. A picking mechanism removes the fruit and moves it to the ground-platform collection system.
Harvesting and crop data
According to Tevel, the platform can record the number, weight, size, color grade, timestamp and geolocation of picked fruit. That information can help managers monitor bin contents and harvesting progress. It should be validated against the farm’s existing grading, traceability and packing workflows.
From trials to orchard deployments
Tevel reports work with growers in the United States, Italy, Chile and Israel, including a Chilean deployment that used eight robots on a ground platform. Apples, peaches, apricots, nectarines, plums and pears are listed among supported crops. Availability and service terms should be confirmed directly.
What the technology cannot solve alone
- Orchard architecture and canopy access affect reach and throughput.
- Fruit occlusion, lighting and weather can challenge vision systems.
- Ground mobility, bin handling and row logistics remain part of the job.
- Selective picking must match the grower’s maturity and quality rules.
- Economics depend on crop value, labor availability, utilization and support.
- A manufacturer deployment is not proof of identical results in every orchard.
What growers should evaluate
Request a crop- and block-specific demonstration. Measure fruit damage, pick rate, missed fruit, uptime, crew requirements, data accuracy and cost per marketable unit. Include maintenance, transport, operator training, insurance and integration with harvest logistics in the comparison.
Primary sources: Tevel product overview · Tevel technology description · Tevel–Unifrutti deployment
About this update: This article was substantively rewritten around the system’s current design, reported deployments and an evidence-focused evaluation checklist.
How many 900 lb. bins of fruit can one drone pick and can they get them into whatever is going to carry them to warehouse coldstorages with out bruising them, because they have to be laid doun and not dropped!