|The overarching goal of ROMI is to develop an open and lightweight robotics platform for microfarms (densely-planted, intermixed polycultures), especially for weed reduction and crop monitoring.
|Fab City Research Lab (Green Fab Lab)
|Sony Europe Limited – Sony (UK), France Europe Innovation – FEI (FR), Institut National de Recherche en Informatique et Automatique – INRIA (FR), Centre National de la Recherche Scientifique – CNRS (FR), Humboldt – Universitaet zu Berlin (DE), SCEA Pepinieres Chatelain (FR).
The Institute for Advanced Architecture of Catalonia coordinates the EU project Robotics for Microfarms (ROMI) under the framework H2020. IAAC is part of a consortium formed by a team of interdisciplinary experts in computer science (Inria, Sony), robotics and electronics (UBER, Sony, IAAC), plant modelling and agronomy (CNRS, Inria), as well as microfarming (Châtelain) which will be in charge of developing ROMI initiative, an open a lightweight robotics platform for small farming land areas.
By implementing robotics in farmlands, ROMI will assist in weed reduction and crop monitoring and it also will help in reducing manual labour, saving farmers 25% of their time. The technology applied in this project will acquire detailed information on sample plants and will be coupled with a drone, developed by Noumena, that acquires more global information at crop level.
Robotics for Microfarms will produce an integrated, multi-scale picture of the crop development that will help the farmer monitor the crops to increase efficient harvesting. This project aims to adapt and extend state-of-the-art land-based and air-borne monitoring tools to handle small fields with complex layouts and mixed crops. IAAC in collaboration with an international consortium will develop and bring to the market and affordable, multi-purpose, land-based robot, integrated 3D plant analysis in the robot for detailed plant monitoring, an aerial NERO drone for multi-scale crop monitoring and test the effectiveness of this solution in real-world field conditions.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 773875