Climate change threatens the productivity and sustainability of food production systems in Africa.
Afri4Cast provides scientific and technical support to Food Security and Safety policies in Africa, for climate change impact analyses.
AFri4Cast contribution is accomplished by making remote sensing data fit the spatiotemporal scale of agriculture, through the use of AI, for Hyperspectral/ Thermal image downscaling and crop parameter estimation from image spectroscopy. This is achieved with the application of Data Fusion and Radiative Transfer Model Inversion methods and technologies on ECOSTRESS and PRISMA images.

Food Security

Rust disease has caused substantial income losses, which were estimated to be in the order of 100s of millions USD per year (2010-2019).
Afri4Cast monitors wheat, maize and rice growth, meteorological events and their effect on crop production and rust disease outbreaks, employing satellite remote sensing products, near-real-time (NRT) meteorological observations and agro-meteorological modelling.

Safeguarding Food safety and Security !

Crop and Irrigation Model

Having as input variables of the environmental conditions, the MODEL runs under different climate scenarios and provides output on the biomass and crop yield, allowing the understanding of how crops respond to environmental changes and yield gap analysis.

Food Safety

Mycotoxins are potential threats to human and animal health, as well as to crop productivity with economic side effects, leading to the development of maximum tolerated limits for mycotoxins in various countries. Even with the standards in place, in 2004, was recorded the greatest fatal mycotoxin-poisoning outbreak caused by contamination of maize with aflatoxins in Africa.

Afri4Cast predicts the risk level of key mycotoxins (deoxynivalenol-DON and aflatoxin-AFB1) in cereal crops and identify high infection risk areas.

AFRi4Cast logic

Decision making and planning measures for food security and safety require critical information for determining crop health and productivity. Monitoring of several indicators related to crop growth, yield, irrigation, and the spread of disease is key for agricultural decision making at all levels, from farm management to adaptation strategies and relief scheme. The frequent supply of such critical information is urgently required in order to make advance monitoring methods operational.

Afri4Cast provides national-, regional-, parcel-, pixel-specific in season production estimates, mycotoxin formation risk and disease outbreak probability. Apart from the in-season yield forecast production line, AFRI4CAst executes seasonal and long-term model simulations for multiannual yield predictions and mycotoxigenic fungi contamination risk under various climate scenarios at a coarse spatial scale.

3 Crops

maize, rice, wheat

2 diseases

mycotoxins & rust

2 countries

Kenya & Uganda

The Afri4Cast Stakeholders


Afri4Cast Deliverables!

This deliverable encompasses a comprehensive overview of ECOSTRESS and PRISMA data, along with an in-depth analysis of current advancements in hyperspectral and thermal data fusion techniques. Additionally, it delves into various methods for retrieving Leaf Area Index (LAI).

This policy review outlines accepted and currently implemented strategies that should be considered in the project’s Design Phase (WP200)

This deliverable outlines the foundational principles, methodologies, and theoretical framework upon which the algorithms and models are built, providing a comprehensive understanding of the design, implementations and expected performance characteristics.

This deliverables summarizes all documentation associated to the developed pipelines (e.g. specifications, architectural design, processing workflows, configuration files, libraries, software reused file, cloud-environment integration manuals)


Three European partners and Six African Participants coming from two African countries are collaborating to support food security and food safety, by  exploiting and building on top of Earth Observation data.


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