Imagine a farm where robots do the heavy lifting and sensors act like a nervous system, telling the farmer exactly when a plant is thirsty or sick. It's like giving a greenhouse a brain to manage itself and a forest a digital map for smarter logging. This tech helps grow more food and wood while using fewer chemicals and less manual labor.
Global food security is threatened by climate change, labor shortages, and fragile supply chains. Current agricultural methods lack the precision and automation needed to increase production while reducing resource waste.
A suite of 7 demonstrators including a Smart Grape DSS, autonomous greenhouse algorithms, and an environment-aware agricultural robot platform for pruning and picking.
If you are a vineyard operator dealing with crop diseases and inefficient resource use — this project developed a Smart Grape Decision Support System that improves productivity and sustainability through better disease detection.
If you are a logging equipment manufacturer dealing with labor shortages and safety risks — this project developed automated forestry capabilities tested in northern Sweden to optimize wood harvesting.
If you are a vertical farm operator dealing with high operational costs and quality control — this project developed systems to increase the efficiency of producing high-quality medicinal plants locally.
Based on available project data, specific pricing or cost structures for the developed technologies are not provided.
The project is currently integrating technologies into 7 use cases and testing demonstrators in real environments, such as vineyards in Italy and forests in Sweden, to validate economic value.
Based on available project data, the specific IP and licensing agreements between the 54 partners are not disclosed.
The project period runs until 2026-02-28, with validation occurring during the upcoming growth seasons.
The project focuses on hardware and software integration across 7 use cases, including robotic platforms for indoor and outdoor use and automated greenhouse algorithms.
The project is heavily industry-driven, with 39 industrial partners (72% of the consortium) including 23 SMEs. This high commercial concentration, led by Komatsu Forest AB, suggests a strong focus on market viability rather than pure academic research. The collaboration spans 15 countries, combining the scale of 16 large enterprises with the agility of SMEs and the technical depth of 16 RTOs.
Contact Komatsu Forest AB in Sweden for partnership inquiries.
Contact SciTransfer to identify specific technology licenses from the 54 consortium partners.
