Integrated AI-EO-Biotech platform to boost crop nutritional quality and public health

This project proposes an actionable, science-driven platform that links Earth Observation (EO), advanced AI analytics and biotechnological validation to enhance crop nutritional quality and create measurable public health benefits.

The project addresses a frequently fragmented chain - environment - agriculture - nutrition - health - by converting environmental and agronomic signals into targeted interventions that raise the concentrations of micronutrients, beneficial fatty acids and bioactive compounds in food crops.

The system will combine high-resolution satellite imagery, on-farm sensors, soil and climate data with multimodal machine-learning models trained on phenotypic and biochemical lab analyses. The platform predicts nutritional attributes and recommends agronomic, breeding or biotech interventions to farmers, cooperatives and food companies.

The platform will be demonstrated across four pilot regions (two Mexican agro-climates, one EU region, one partner country), covering the full value chain from field management to dietary intake modelling and epidemiological impact assessment.

The project will deliver operational evidence linking agricultural practices and environmental drivers to nutritional outcomes and translate those into estimated reductions of diet-related NCD risk. It is designed as a replicable SaaS for regional authorities, health agencies and industry, with clear exploitation routes and policy guidance for EU and partner countries.

Objectives:

1. Build an operational AI-EO system predicting crop nutritional parameters with ≥80% accuracy.

2. Demonstrate ≥10% improvement in key micronutrient or fatty-acid profiles in at least three crops through validated interventions.

3. Quantify public health impact via exposure-response modelling.

4. Produce policy briefs and decision tools for integrating nutrition-oriented agriculture into health strategies.

Expected results:

• A validated digital platform and API.

• AI models linking EO and in-field data to biochemical markers.

• Four regionally-representative pilots with open FAIR datasets.

• Evidence packs for policymakers and exploitation plans.

KPIs:

• Predictive accuracy ≥80% for nutritional markers.

• ≥10% mean increase in target nutrients across pilots.

• Coverage >1,000 hectares combined.

• Estimated reduction in diet-related risk metrics translated to DALYs avoided.

• At least 3 peer-reviewed publications and FAIR datasets.

Health impact:
By elevating the nutritional quality of the food supply, the platform supports primary prevention of diet-related NCDs, reduces micronutrient deficiencies and strengthens food system resilience under climate change - delivering benefits for vulnerable populations in Mexico, the EU and beyond.

Innovation & application:
The core innovation is operational fusion: satellite environmental intelligence + AI + biotech validation to guide farm-level decisions that deliver measurable health outcomes. The product is a scalable service with clear market channels (agri-businesses, public health agencies, NGOs), and strong policy uptake potential.