Imagine giving plants a voice to tell us exactly how they are feeling and what the soil needs. This project builds smart sensors and uses satellite images to monitor crop health and environment in real-time. It's like having a high-tech health tracker for every field to ensure food grows well even as the weather changes.
Farmers and seed breeders struggle to adapt to climate change because they lack affordable, real-time data on how plants and soil react to environmental stress across large areas.
Low-cost IoT multi-sensor devices and AI algorithms for monitoring soil health, crop diseases, and orchard conditions, alongside an automated satellite mapping tool.
If you are a hardware manufacturer dealing with high production costs for field sensors — this project developed low-cost, AI-powered devices that monitor soil health and crop diseases. This allows you to offer more affordable, scalable monitoring kits to farmers.
If you are a seed breeder dealing with unpredictable climate shifts — this project developed tools to identify future-proofed crop varieties. By using digital twins and AI, you can accelerate the development of seeds that survive extreme weather.
If you are a data provider dealing with low-resolution ground truth data — this project developed an automated method to map field variability using Copernicus Sentinel satellites. This improves the accuracy of your land-use analysis services.
Based on available project data, the project focuses on developing 'low-cost' devices, but specific pricing units are not provided.
Yes, the project uses 8 Use Cases across various agroecosystems and leverages Earth Observation data to move beyond limited installations to wide-access services.
Based on available project data, the project involves 7 industry partners and 3 SMEs, but specific licensing terms are not detailed in the summary.
The project develops IoT multi-sensor devices and algorithms designed for in-situ use on farms and in natura settings.
The project period runs from 2023-01-01 to 2027-12-31, indicating a multi-year development and testing cycle.
The consortium is heavily weighted toward research and academia (22 partners from universities and research centers), but maintains a significant commercial footprint with 7 industry partners and 3 SMEs. This 22% industry ratio suggests a strong bridge between theoretical AI/modeling and practical market application, with a broad geographic reach across 11 countries.
Contact the Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE) in France.
Contact us to connect with the PHENET industry partners for early access to AI-powered phenotyping tools.
