Predicting the contamination of eatable plants with AI
SmartUrbAgri aims to improve the prediction of pollutant transfers from soil to plants in order to improve the methodology for carrying out Quantitative Health Risk Assessments in the context of Urban Agriculture projects.
The project aims to achieve a technological and methodological innovation through the development of an AI-based tool to predict pollutant concentrations in plants adapted to the specificities of Urban Agriculture.
SmartUrbAgri will thus allow a more appropriate prediction of the transfer of contaminants from soil to vegetable plants in the context of urban agriculture projects, leading to safer control of health risks.
Experts from Ixsane's innovation Department will develop AI algorithms to assess the transfer of metallic and organic pollutants in vegetable plants grown on contaminated soils in urban and peri-urban contexts. For this, data from databases such as BAPPET & BAPPOP and data from specific experiments will be used.
The teams of Ixsane's engineering Department will evaluate these models against current approaches to improve the operational practices of Quantitative Health Risk Assessments of Urban Agriculture projects.
Start date: June 2025
End date: June 2028
Partners:
The GRANUL'IA project is supported by theHauts-de-France ERDF Programme 2021-2027.
The SmartUrbAgri project is the winner of the GRAINE2023 Call for Projects funded by Ademe.
Total project budget: €485,723
including total ADEME funding :€299,920
