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AgTech  ·  Future of Food  ·  Robotics & Automation

Mechanical weeding in pumpkins: 
The Dutch pilot excels!

The Robs4Crops’ Dutch pilot has developed a new way to precisely hoe crops of pumpkins using a combination of the Robotti robot and a smart steered camera sideshift from WIFO. With a robot in control of the quality of weeding, farmers will be able to focus on other tasks. Additionally, the use of smart implements behind a Robotti will increase efficiency and accuracy in crop management.

This new method is a major step towards the ultimate goal of the project, which is to develop a monitoring system for use behind a weeder. The Dutch team will use the data collected from the previous year to further develop the algorithm and that will ultimately lead to a more efficient and effective monitoring system.

In 2023, the team plans to hoe sugar beets, since the potential for autonomous weeding is much bigger compared to pumpkins.  Additionally, the team will be conducting tests and showcasing the initial version of an automated algorithm for weed detection. The algorithm is designed to detect any errors in the weeding process, and in such cases, it will trigger the Robotti to stop operation and alert the operator.

The Robs4Crops project is paving the way for a future in which robotics play a major role in agriculture, increasing food production and helping farmers work more efficiently. This is an exciting development and we can’t wait to see what the future holds for this technology!


The future of robotized weeding is getting closer
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About Robs4Crops

Robs4Crops is a four-year project co-funded by the European Commission to accelerate the shift towards the large-scale implementation of robotics and automation across the European farming landscape.

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Project Coordination

Wageningen University & Research
Department of Agrosystems Research
PO Box 16
6700 AA Wageningen
The Netherlands
info@robs4crops.eu

Project Communication

Foodscale Hub Narodnog fronta 73 21000 Novi Sad Serbia foodscalehub.com

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101016807.

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