AI for Food Challenge

Developing high-precision agricultural robots that will increase land yield while reducing resources, with use of digital twins and reinforcement learning.


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Making training and testing of farming robots faster and more affordable

Artificial Intelligence (AI) and robotics can help farmers to produce higher-quality food and have less impact on the environment. However, training and testing of agriculture robots is time consuming and expensive, because this usually happens in a physical environment and often involves the robot breaking down. In our project, we create a digital version of the robot that is tested and trained in a simulated environment. This way, training and testing of robots becomes faster and cheaper.

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Our challenge partner

GOAL: Developing a framework for training and testing of autonomous robots in agriculture

In this project we will focus on: 

  • developing a framework for training and testing of autonomous robots, focussed specifically on the agricultural domain;
  • and building a demo setup for validating the applicability of the framework to a real-world setting.

The testing setup of Agrosim with a custom made physical and digital training environment for the turtlebot with gripper, Intel rear sense camera and lidar

We will do so by building a demo setup with a robot in an environment that will mimic a real-world scenario. At the same time we will train a digital twin of this robot in a simulated environment. We will:

  • Build the environment based on sensor information of the robot.
  • Develop object detection of fruits and mapping of surroundings for navigation with the help of minimal sensory information.
  • Integrate the ability to use own, pre-trained object detection algorithms
  • Set up a training framework in the simulated environment in order to teach the robot correct behavior.

The demo has been a success when the robot is able to ..

  • navigate smoothly within the physical environment without damaging itself
  • detect fruits and classify them correctly
  • grasp the fruit based on the sensory information it has
  • Autonomously collect fruits, based on easy command

Did you know

🥥   By 2050 there are expected to be 9 billion people on Earth. The planet must produce more food in the next four decades than all farmers in history have harvested over the past 8,000 years.

🥝  Industrial farms don’t support the rich range of life that more diverse farms do. As a result, the land suffers from a shortage of the ecosystem services, such as pollination, that a more diverse landscape offers. 

🥥   Robotic solutions such as pixelfarming can increase the biodiversity and proper usage of the soil, decreasing the weight of the machinery used for managing and cultivating this soil. 

🍉   Farmers’ interest in the latest technologies has turned agricultural robots and drones into a market that’s set to reach $23.06 billion by 2028. 

Application deadline

June 10, 2021
To application page


Application Deadline: 20 June 2021

Final Presentations: 6 August 2021

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