EM Grow

Contract number: 000144836/24/NL/GM/jxh

Status: On Going

EM-GROW

EcoMeadow: GNSS-Led Herbicide-Free Precision Weed Management in Grassland with Drones and Robots

About us

Experience the future of organic farming with a pioneering herbicide-free weed control solution. Utilizing advanced GNSS and RTK technology, an autonomous robotic fleet precisely targets and removes weeds, optimizing yield and ensuring livestock safety. Paltech’s Robots as a Service (RaaS) model delivers efficient, sustainable, and resource-effective farming, revolutionizing weed management for organic farmers in Germany.

User and their needs

The primary costumers are organic farmers, both dairy and horse farms, managing permanent grassland in Germany with nearly 85% of organic farmers managing grassland facing issues with dockweed.

  • Resource-Efficient Solutions: Farmers need weed control methods that are less resource-intensive in terms of water and energy usage.
  • Yield Optimization: Farmers need effective strategies to minimize the negative impact of Dock on yield to maintain or enhance milk production.
  • Livestock Safety: Farmers need reliable methods to prevent livestock from consuming Autumn crocus to avoid potential fatalities.
  • Efficient Weed Control: Farmers need less strenuous and time-consuming weed control methods to reduce physical strain and back pain.
  • Herbicide-Free Options: Farmers need effective, non-herbicidal weed management solutions to comply with organic farming regulations and maintain subsidies, such as Bavaria’s €273/ha.

Objectives of the service

Paltech’s service addresses these issues by eliminating the need for manual labor through a completely herbicide-free, mechanical extraction process. Utilizing space technology, the robotic fleet efficiently scours fields, targeting and removing weeds one by one.

The main problems of the users are related to the negative impact of dock (Rumex) on milk production. In addition, by adopting organic methods of maintenance, strenuous and time-consuming manual extraction processes are incurred, which consume large amounts of time and cause physical pain.

This project aims to validate the accuracy and efficiency of the GNSS-enhanced robotic weed detection and removal system, assess user satisfaction with the service—focusing on ease of use and reliability—and evaluate the environmental benefits of reduced herbicide use.

Service and System concept

The service starts with four robots, which are brought to the field and unloaded. Before the actual weed control begins, a drone flies over the field and takes pictures at strategic locations. These photos are used to detect and locate the weeds. Thanks to RTK positioning, this step may also have happened before (e.g. before cutting). This allows optimized routes to be calculated on which the robots travel and remove the weeds. Once a robot has reached the right spot, it positions itself over the plant and removes it. It then sows grass seeds to restore the turf and ensure sustainable control. Paltech adopts a Robots as a Service (RaaS) model, forging partnerships with contractors to address logistical challenges.

Space added value

Satellite-based positioning systems, particularly GNSS and RTK, significantly enhances a herbicide-free weed control solution. It ensures precise localization and navigation of the drone and the robotic fleet, since traditional weed control methods, such as manual labor or broad application techniques, often lack precision, leading to inefficiencies. With the local RTK base station the robots can ensure minimal positioning errors, allowing to bypass the need to build an orthomosaic with the drone images and passing on the positions of the detected weeds directly to the robots which can be time-consuming and resource-intensive.

This allows to tracing the weeds over the years and maximizing transparency, traditional methods often lack the capability to track weed growth and distribution over time, making long-term weed management less effective.

Finally, this allows to optimize the paths and maximizes efficiency, reducing operational time, this optimization translates to lower labor costs, reduced wear and tear on equipment, and faster completion of weed control tasks.

Current Status

Significant progress has been made in refining the technical aspects of the project, including the integration of new sensors and advancements in autonomous field operations. The team is actively addressing challenges related to network connectivity and sensor performance to enhance overall system reliability.