Periodic Reporting for period 1 - SOSFood (Sustainability Optimization for Secure Food Systems)
Reporting period: 2024-02-01 to 2025-07-31
EU food systems must become more inclusive, sustainable, resilient and productive. SOSFood uses data-exploitation and AI-based methods to comprehensively map the food system and develop tailored predictive tools to improve decision-making processes along the food chain, with a multi-factorial, -actor and -scale approach, resulting in a consolidated food data space focused on sustainability and health with decision-making tools adapted to each level of the chain, by: ·creating a multi-actor network with expertise from health and nutrition to socioeconomic factors of consumption and environmental impact of food production; ·mapping the food system scenario with a multidimensional strategy, exploiting interoperability of data with advanced impact analysis and innovative AI-technologies; ·co-designing solutions validated through field case studies to ensure validity.
The main objectives are to co-create a multi-actor and social innovation approach to analyse needs and expectations of stakeholders to improve sustainability; explore existing private data sharing systems in the food supply chain, adapt the solutions to each stakeholder; design a framework to measure impacts of the food system on sustainability, using methods and metrics for the interaction among the defined systems, new factors and interactions derived from applying AI for a dynamic multimodal impact analysis; develop predictive and clustering analysis methods based on machine learning to identify and predict the evolution of factors with impact on the sustainability of each stage of the food system; develop digital twins to predict and optimize the relevant indicators from a multi-stakeholder perspective; develop and implement data-driven decision-making tools for nutritional and sustainable food systems; validate project innovation through different agri-food systems and demonstrating applicability in other EU communities; maximize the impact of the project through dissemination, communication and exploitation.
The main objectives are to co-create a multi-actor and social innovation approach to analyse needs and expectations of stakeholders to improve sustainability; explore existing private data sharing systems in the food supply chain, adapt the solutions to each stakeholder; design a framework to measure impacts of the food system on sustainability, using methods and metrics for the interaction among the defined systems, new factors and interactions derived from applying AI for a dynamic multimodal impact analysis; develop predictive and clustering analysis methods based on machine learning to identify and predict the evolution of factors with impact on the sustainability of each stage of the food system; develop digital twins to predict and optimize the relevant indicators from a multi-stakeholder perspective; develop and implement data-driven decision-making tools for nutritional and sustainable food systems; validate project innovation through different agri-food systems and demonstrating applicability in other EU communities; maximize the impact of the project through dissemination, communication and exploitation.
Project management
·Procedures and ethical guarantees
·Data Management Protocols to ensure data safety
Stakeholder engagement
·Multi-actor network
Scientific research
·Identification of databases and private data providers for multidimensional analysis of the project
·Identification of EU initiatives and projects mutually benefitting from data sharing and reutilization initiatives (geographical and scientific scope)
·Data collection procedures
·Data sharing matrix for definition of data sharing types
·Literature review for a theoretical framework from nutritional, economic, consumer health aspects
·Analysis of data sharing systems
·Collection of innovative measures present in agri-food sector
Software development
·Definition of user cases and requirements for app and dashboard; first prototypes
·Definition of requirements for AI-based data exploitation services
·First version of GDPR-compliant platform integration with data lake through APIs
Software testing
·Development of first software and UI/UX tests
·Establishment of validation protocols in all case studies
·Validation of user cases
·Procedures and ethical guarantees
·Data Management Protocols to ensure data safety
Stakeholder engagement
·Multi-actor network
Scientific research
·Identification of databases and private data providers for multidimensional analysis of the project
·Identification of EU initiatives and projects mutually benefitting from data sharing and reutilization initiatives (geographical and scientific scope)
·Data collection procedures
·Data sharing matrix for definition of data sharing types
·Literature review for a theoretical framework from nutritional, economic, consumer health aspects
·Analysis of data sharing systems
·Collection of innovative measures present in agri-food sector
Software development
·Definition of user cases and requirements for app and dashboard; first prototypes
·Definition of requirements for AI-based data exploitation services
·First version of GDPR-compliant platform integration with data lake through APIs
Software testing
·Development of first software and UI/UX tests
·Establishment of validation protocols in all case studies
·Validation of user cases
Food data space-data collection procedures; data management platform and a data lake, both compliant with GDPR. Implemented in three EU case scenarios (Athens, Galicia, Lithuania).
Metrics for food system assesment, integrating nutrition, health, social, economic and environmental dimensions to be displayed through an eco-healthy fingerprint visualization plot.
Analytical methods based on ML to manage heterogeneous data from diverse actors in the food system; reduce computational costs; and provide interpretable and explainable results (xAI).
Decision-making tools:
·Predictive Dashboard: allows administrations, companies and consumers to address their policy-, business- and consumption-choices extending sustainability in the food system with a robust scientific grounding. This approach identifies which actors can influence or direct this transformation.The predictive AI-empowered methods will enable decision makers to underpin their decisions with a solid scientific basis, built from evidence, with an appropriate quantification of the ensuing uncertainty, and in a comprehensible manner.
·Consumer mobile app: integrates AI-based insights, eco-healthy fingerprints, and reformulated diets, continuously improved with user feedback, thus linking technical achievements to societal impact.
The first prototypes have been developed and validated for their suitability to the identified user stories and their fitting to the case studies: predictive analysis of time series data, estimating evolution and interaction of social, political, legal, economic, technological, food and climate drivers that affect sustainability; clustering analysis of food system trajectories, identifying alternative pathways across production, transformation, consumption, and waste; digital twins to simulate the impact of sustainability indicators at midpoint and endpoint levels; causal discovery techniques, linking high-level economic indicators, regulations, and production trends to their environmental and social impacts; xAI approaches, combining expert knowledge with machine learning outputs to ensure transparency, multilingual accessibility, and trustworthiness of results.
These developments respond to needs identified in the field: data exploitation, integration of multimodal datasets, and provision of decision-support tools that are robust and interpretable. By embedding software sustainability principles and ethical guidelines into system design, SOSFOOD ensures that its AI solutions are not only technically advanced but also trustworthy and usable in real-world contexts.
Metrics for food system assesment, integrating nutrition, health, social, economic and environmental dimensions to be displayed through an eco-healthy fingerprint visualization plot.
Analytical methods based on ML to manage heterogeneous data from diverse actors in the food system; reduce computational costs; and provide interpretable and explainable results (xAI).
Decision-making tools:
·Predictive Dashboard: allows administrations, companies and consumers to address their policy-, business- and consumption-choices extending sustainability in the food system with a robust scientific grounding. This approach identifies which actors can influence or direct this transformation.The predictive AI-empowered methods will enable decision makers to underpin their decisions with a solid scientific basis, built from evidence, with an appropriate quantification of the ensuing uncertainty, and in a comprehensible manner.
·Consumer mobile app: integrates AI-based insights, eco-healthy fingerprints, and reformulated diets, continuously improved with user feedback, thus linking technical achievements to societal impact.
The first prototypes have been developed and validated for their suitability to the identified user stories and their fitting to the case studies: predictive analysis of time series data, estimating evolution and interaction of social, political, legal, economic, technological, food and climate drivers that affect sustainability; clustering analysis of food system trajectories, identifying alternative pathways across production, transformation, consumption, and waste; digital twins to simulate the impact of sustainability indicators at midpoint and endpoint levels; causal discovery techniques, linking high-level economic indicators, regulations, and production trends to their environmental and social impacts; xAI approaches, combining expert knowledge with machine learning outputs to ensure transparency, multilingual accessibility, and trustworthiness of results.
These developments respond to needs identified in the field: data exploitation, integration of multimodal datasets, and provision of decision-support tools that are robust and interpretable. By embedding software sustainability principles and ethical guidelines into system design, SOSFOOD ensures that its AI solutions are not only technically advanced but also trustworthy and usable in real-world contexts.