Profile
Agrobot was founded in 2008 with the specific mission of designing a sophisticated strawberry harvesting machine to address critical labor shortages and rising costs in the strawberry industry. We’ve spent years pushing the boundaries of agricultural technology to perfect our robotic harvesters. Our newest model is a breakthrough in precision: an AI-driven machine with modular arms that can harvest delicate strawberries by the stem with unmatched care.
Agrobot established itself as a trailblazer in 2018 by being one of the first companies to deploy deep learning and advanced computer vision for real-time, selective harvesting in open fields. Long before AI became a mainstream industry standard, the company’s autonomous harvesters were already using convolutional neural networks to perform complex semantic segmentation, allowing robotic arms to distinguish ripe fruit from leaves with human-like precision.
Since 2024, we have integrated high-efficiency solar arrays to power our autonomous fleets, allowing them to run entirely on renewable energy. Our vehicles operate continuously without the need for manual charging, providing a sustainable solution across a wide range of farming applications.
Robotic Arms
In agricultural applications like precise weeding, pruning or harvesting, sometimes industrial manipulators are not the most suitable choice, since they do not meet certain project requirements such as cost, dimensions, power or weight. We design robotics manipulators , whether Linear, Cartesian, or more complex configuration like SCARA or Articulated.
Mobile Robots
All-terrain mobile agricultural operations demand rugged and solid designs to achieve appropriate levels of reliability. Our experience leads us to create lean and ease-to-manufacture solutions. From simpler approaches like differential steering drive to more elaborate ones, e.g. skid or omnidirectional, we conceive practical platforms to carry implements or any other devices that final applications requires.
Software Development
Our team’ skills rotate around perception, positioning and motion. Machine learning running in embedded system is the core of our vision capabilities, like object classification, segmentation and instantiation. Sensors integration allow us to analyze distances, poses, surfaces and volumes and create motion control systems.
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