Imagine a digital health tracker for wheat, but instead of a wristband, it uses sensors in the dirt and scanners on the plants. It follows the grain's journey from the field all the way to the flour mill to make sure it's healthy and safe. This helps farmers and millers know exactly what's happening with their crop without having to guess.
Wheat production often suffers from a lack of connected data between soil health, plant growth, and final grain quality. This gap leads to inefficiencies in farming and difficulties in ensuring food safety and traceability.
A digital soil monitoring system, a cloud-based Decision Support System, and machine learning models for wheat traceability.
If you are a software provider dealing with inaccurate soil nutrient data — this project developed a digital soil monitoring system that uses machine learning to assess chemical and biological factors. This allows for more precise fertilizer application and better crop health.
If you are a mill proprietor dealing with inconsistent grain quality — this project developed a traceability system that tracks wheat from growth to production. This ensures the safety and nutritional value of the final flour product.
If you are a hardware company dealing with low adoption of sensing tools — this project developed a Decision Support System and cloud platform that integrates remote and proximal sensors. This makes complex data accessible and useful for end-users.
Based on available project data, specific pricing or cost structures are not provided as the project is currently in the implementation phase.
Yes, the project specifically uses automated mapping techniques and machine learning models to boost efficiency and scalability across multiple European regions.
Based on available project data, the specific licensing terms are not listed, but the project involves a consortium of 15 partners including 3 industry members.
The system leverages a cloud platform and a Decision Support System to integrate data from various remote and proximal sensing technologies.
The project period runs from 2024-10-01 to 2028-09-30, suggesting a multi-year development and testing cycle.
The project is backed by a diverse 15-partner group across 10 countries, showing strong international cooperation. With a 20% industry ratio (3 companies), there is a clear link to commercial application, though the heavy presence of 7 universities and 4 research centers indicates the project is currently driven by technical development and validation.
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