Autonomous Navigation in Agriculture

Our team has developed a robotic self-driving tractor that can be used for carrying objects, sowing, weeding, making furrows. It uses the images of two onboard cameras and also takes advantage of the regular structure of the rows in the fields for navigation. This allows the robotic ride-on mower to accurately follow the rows and manage the transition to the next row with a solution of continuity.
The software is based on C ++ and ROS and has also been tested in different environments, both in simulation and in the real world and on different robotic platforms.

Autonomous tractor navigation software uses artificial intelligence (AI) and machine learning algorithms to enable tractors to operate without human intervention. Here are some ways AI can be used in autonomous tractor navigation software:

  1. Computer vision: AI-powered cameras and sensors can be used to detect obstacles and navigate the tractor through complex environments such as fields and farm roads.

  2. Route planning: AI can be used to plan the most efficient route for the tractor based on factors such as crop type, soil conditions, and weather data.

  3. Predictive maintenance: AI can monitor the tractor’s performance and predict when maintenance is needed, reducing downtime and increasing efficiency.

  4. Yield mapping: AI can be used to collect and analyze data on crop yield, allowing farmers to make more informed decisions about crop management.

  5. Real-time monitoring: AI can monitor tractor performance in real-time, allowing farmers to adjust tractor speed and direction to optimize crop yield.

  6. Data analysis: AI can analyze data collected from sensors and cameras to provide farmers with insights into crop health, soil conditions, and weather patterns.

Techical features

Autonomous tractor navigation software features are:

  • No map or location required.
  • Running on controllers with limited processing power (Raspberry).
  • An integrated computer with Intel Core i5 processor with Linux operating system, ROS
  • 24V lithium batteries.
  • Gazebo simulation environment.
Autonomous tractor with stereo vision
autonomous vehicle software
wheels robotics

Technology at the service of agriculture

The robotic ride-on mower also offers the possibility of using high-precision GPS and artificial intelligence to follow people and also to autonomously navigate from point A to point B while carrying a load.

The robotic ride-on mower can be equipped with various accessories that allow, for example, the treatment of some pests/diseases.

Contact us if you are a farmer and would like to experiment with some of these technologies.

Agriculture and AI have the potential to transform the farming industry by increasing efficiency, reducing costs, and improving crop yield. Here are some examples of how AI is being used in agriculture:

  1. Precision farming: AI-powered sensors can be used to monitor soil conditions, crop health, and weather patterns. This data can be analyzed to optimize irrigation, fertilization, and other farm management practices.

  2. Autonomous tractors: AI-powered autonomous tractors can navigate through fields, plant and harvest crops, and perform other tasks without human intervention.

  3. Crop monitoring: AI-powered cameras and sensors can monitor crop growth and detect diseases and pests, allowing farmers to take proactive measures to protect their crops.

  4. Predictive analytics: AI can analyze data on soil conditions, weather patterns, and other factors to predict crop yield and potential issues such as drought or disease outbreaks.

  5. Livestock monitoring: AI can monitor animal behavior and health, detecting issues such as stress or illness before they become serious.

  6. Supply chain optimization: AI can be used to optimize the supply chain by predicting demand, reducing waste, and improving logistics.