Our Flying Autonomous Robots™ are driven by cutting-edge guidance and control algorithms that enable incredible accuracy and maneuverability. They continuously collect data on every single piece of fruit they pick, providing extraordinary real-time harvesting data. Our cost effective flying robots are durable and light, allowing for the harvest of a wide range of fruit, from a 50g (2 oz) apricot to a 700g (25 oz) apple. The Flying Autonomous Robot™ is the only one in the world that physically interacts with foliage and is designed to continuously fly in and out of the trees without harm. Tevel's disruptive innovation has been granted 11 patents worldwide.
Benefits For Farmers
Better Planning:
Streamline harvesting logistics
Optimize harvesting activities
Improve yield & quality
Better Business:
Gain control over produce
Boost profit
Increase certainty
REAL-TIME HARVESTING DATA AT YOUR FINGERTIPS
Our data collection technology provides actionable insights and real-time data on the bin before it is sent to the packing house.
Total amount of fruit picked
Weight & size of each fruit
Color grading
Disease detection
Timestamp
Geolocation
Distribution of weight, size & colors in the bin
Tevel Highlighted By Global Media
AI Perception Algorithms
Fruit tracking and data fusion
Vision Algorithms
Detection of fruit, foliage, and other objects. Includes fruit classification (size, ripeness, etc.)
Maneuver Algorithms
Optimal trajectory planning and execution
Balancing Algorithms
Stabilization algorithms to balance the forces applied on the robot by foliage and fruit
Harvesting Optimization
Fleet management optimization algorithms for harvesting based on orchard data
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AI Perception Algorithms
Fruit tracking and data fusion
Vision Algorithms
Detection of fruit, foliage, and other objects. Includes fruit classification (size, ripeness, etc.)
Maneuver Algorithms
Optimal trajectory planning and execution
Balancing Algorithms
Stabilization algorithms to balance the forces applied on the robot by foliage and fruit
Harvesting Optimization
Fleet management optimization algorithms for harvesting based on orchard data
