AmAIzed: AI-Assisted Multi-Tasking Robot for Sustainable Agriculture
Overview
AmAIzed is a collaborative initiative between the Technical University of Munich (TUM), Weihenstephan-Triesdorf University of Applied Sciences (HSWT), and the Bavarian State Research Center for Agriculture (LfL), funded by the AgroMissionHub. The project aims to develop a multi-tasking AI-assisted robotic system for individualized management of crops and weeds.
Objectives
- Reduce pesticide usage: Aligning with EU targets to cut the use and risk of chemical pesticides by 50%.
- Enhance precision agriculture: Utilizing AI and robotics to manage crops and weeds at the individual plant level.
- Promote sustainable farming: Minimizing soil compaction and environmental impact through lightweight robotic solutions.
Methodology
The project brings together experts in:
- Agro- and Bio-Engineering
- Crop & Horticulture Science
- Plant Protection
- Farm Management
By integrating state-of-the-art technologies such as integrated pest management (IPM) and sensor-based variable rate herbicide application, AmAIzed seeks to overcome the limitations of traditional mechanical weeding and contribute to more sustainable agricultural practices.
Project Partners
- Technical University of Munich (TUM): Prof. Kang Yu (Coordinator), Prof. Heinz Bernhardt, Prof. Mirjana Minceva
- Bavarian State Research Center for Agriculture (LfL): Klaus Gehring, Stefan Kopfinger
- Weihenstephan-Triesdorf University of Applied Sciences (HSWT): Prof. Simon Walther, Prof. Thomas Ebertseder, Prof. Michael Beck
Publications
- Lu et al. (2025). Weed instance segmentation from UAV Orthomosaic Images based on Deep Learning. Smart Agricultural Technology. View
- Zhang et al. (2024).Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency. Remote Sensing. View
For more information, visit the AmAIzed project page at HEF.
Prof. Dr. Kang Yu
Professor of Precision Agriculture
My research interests include precision crop farming, hyperspectral remote sensing, and AI in agriculture.
Chenghao Lu
Research assistant & Joint Phd student
My research interests are Agricultural Digital Twin and Smart Breeding. My project focuses on the construction of a Digital Twin system for maize, aiming to fuse Genetic x Environmental x Management data to construct a high-precision and dynamically updated maize growth virtual twin. By introducing a DNA language model with a multimodal fusion approach, I hope to achieve accurate simulation of maize phenotypes and breeding to support sustainable agricultural development.
Shuai Yang
PhD student
My research focuses on Deep learning, hyperspectral remote sensing, and radiative transfer model.
