SentiV is a scouting robot that highlights the variability within field crops plots and detects potential threats to crops.

The SentiV scouting robot, a prototype spoke-wheeled plant nurse, is being developed to inspect acres of row crops for disease, pests, and other issues.

It can examine up to 50 acres per day of a farmer’s fields.

It moves like a tumbleweed and uses GPS coordinates to map the field, scanning plants with cameras and using smart algorithms to identify threats and monitor growth. The adjustable wheel height ensures it doesn’t damage the crops.

This technology has the potential to save farmers time and money by reducing the need for manual inspections and pesticides.

Capabilities

• Autonomous navigation – Once in place, SentiV detects where it has been setup and moves without an operator nearby. The plot is surveyed and analyzed in its entirety, covering up to 20 hectares per day.
• Scouting & Harvesting data – The robot has two cameras that scan over and under the foliage. With time we plan to add other sensors according to the types of data to be detected.
• Modularity – In order to adapt to the type of plants studied and to follow the growth of crops, we have made SentiV a modular robot: width, height, mode of motion are adjustable.
• Accuracy of readings – Unlike existing remote sensing solutions, images captured by the SentiV sensor provide more details than can be seen by a human eye on the plot. Besides, the robot also scans under the crop canopy (soil and under leaves).
• Preservation of crops – A robot that does not damage crops was the leitmotif that guided the development of SentiV. In addition to its lightness (15kg), SentiV moves thanks to a unique and innovative wheel system to step over vegetation.
• Obtaining qualified information – Collected data are analyzed by artificial intelligence algorithms allowing to:

1. Monitor nutrient and water requirements of crops for variable rate seeding or spraying.
2. Identify the presence of biological threats: weeds, diseases, and pests (invertebrates, birds, mammals).
3. Know the phenological stages for crop growth monitoring.
4. Optimize phenotyping.

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Image Credit: Meropy