Project Background: The Origins of aimGrassland

aimGrassland ("Artificial Intelligence for Maintenance of Grassland") builds upon two projects that complemented each other perfectly: the EIP-Agri project Antago Senecio and the collaborative research project KIhUG. By combining the insights from both initiatives, we have developed an approach to support future grassland management in a data-driven, selective, and chemical-free manner.

Starting Point 1: Antago Senecio – Integrated Strategies Against Ragwort

The Antago Senecio project focused on a central challenge: Ragwort (Jacobaea vulgaris, syn.: Senecio jacobaea) frequently occurs on extensively managed grassland and can compromise its use as fodder due to toxic pyrrolizidine alkaloids. The goal was to develop nature-friendly and integrated strategies to regulate ragwort while preserving species-rich meadows.

Key Learnings from Antago Senecio

  • In practice, reliable monitoring is essential to detect the occurrence and dynamics of problematic plants at an early stage.
  • Grassland management often lacks selective, efficient tools that preserve accompanying vegetation and reduce conflicting objectives (e.g., nature conservation requirements vs. cutting times).

Starting Point 2: KIhUG – AI-Based Detection as a Technological Foundation

To address the need for digital tools, the KIhUG project was established as a collaboration focusing on the AI-supported detection of weeds. The research focused on:

  • The generation of training data (including synthetic data),
  • The training of artificial neural networks for reliable detection,
  • System integration and robotics concepts to derive targeted control measures from detection data.

Why a New Project Was Necessary: From "Detecting" and "Understanding" to "Applying"

Antago Senecio primarily provided the ecological, biological, and agronomic foundations, as well as a strong network of practitioners. KIhUG created the technological basis for automatically detecting problem plants. However, it became clear that a practical solution requires both: ecological system understanding and robust digital technology that functions reliably in the field.

This is precisely where aimGrassland steps in: We combine the experience from both projects to further develop monitoring and decision-making in grassland management in a site-adapted, efficient, and environmentally friendly way (integrating sensors/drones, AI analysis, and future control tools).

What aimGrassland Adopts from its Predecessors

  • Practical Relevance: Research questions and field tests are directly oriented toward the real-world challenges of grassland farms.
  • Evidence-Based Foundations: Management approaches are rigorously evaluated for ecological compatibility and effectiveness.
  • Digital Feasibility: AI models, data collection, and system logic are designed for practical application (e.g., generating maps/overviews as a basis for decision-making).