Summer School on Affordable AI (SAAI)

Wed, 01 May 2024 00:00:00 +0000

Artificial intelligence could ease the shortage of specialists and diagnostic capacity in many health systems, yet most AI courses assume powerful hardware, large datasets and stable infrastructure that are scarce in low- and middle-income countries of the Middle East and North Africa (MENA). The Summer School on Affordable AI (SAAI, سعي — “striving”) was designed to close this gap by teaching AI that works under real-world resource constraints.

SAAI took place in Bonn, 22–26 July 2024, and was organised by the AGYA working group Innovation, in close collaboration with the working group Health & Society, of the Arab-German Young Academy of Sciences and Humanities (AGYA), with the University of Bonn, the University Hospital Bonn and Helmholtz AI. It was hosted by the Albarqouni Lab and modelled on our earlier .

Programme

Participants: about 30 students and graduates and 12 faculty members from Germany and Arab countries, selected through an open call.
Foundations: Python programming, mathematics and statistics for machine learning, and analytical thinking.
Affordable AI: teaching modules tailored to the needs and resource limitations of low-income MENA countries, addressing shared health challenges in Germany and the Arab world.
Hands-on learning: lectures, hands-on labs and real-world challenges, worked on in mixed teams with faculty and experienced tutors.

Outcomes

Participants built practical skills in developing AI models for health under limited compute and data, and the school fostered knowledge transfer and collaboration between young researchers from Germany and the Arab world. SAAI laid the groundwork for the DAAD-funded and the in Tunis in 2025.

AI for predicting the technological and phenolic maturity of pomegranates in Lebanon

Sun, 01 Jan 2023 00:00:00 +0000

Pomegranates are one of Lebanon’s important fruit crops, and the date on which they are harvested decides both their health value — their content of antioxidant polyphenols — and their market value. Farmers usually harvest on experience or by the calendar, without accounting for how strongly the climate and the growing region shape fruit maturity, and the laboratory measurements that would tell them are slow and need equipment most farms do not have.

Bringing together food science at Beirut Arab University (Nada El Darra) and AI at the University of Bonn, this interdisciplinary project of the Arab-German Young Academy of Sciences and Humanities (AGYA) asked whether simple, cheap measurements can predict the quality of the fruit before it is harvested.

What we did

A unique field dataset. Over three months of the 2023 season (September–November), 548 sweet pomegranates were harvested from three regions of Lebanon — Hasbaya, El Jahliye and Rachiine. Each fruit was photographed with a smartphone under controlled conditions and weighed and measured, and its juice was analysed in the laboratory for total soluble solids, titratable acidity, maturity index, polyphenol content and colour intensity. Climate data (temperature, solar radiation, humidity, precipitation) were added for each site, with support from the Lebanese Agricultural Research Institute (LARI).
Predicting quality from simple features. Linear regression and multi-layer perceptron models predicted the food-quality indicators from physical, geographical and environmental features. The MLP reached an R² of 0.84 for titratable acidity, 0.70 for phenolic content and 0.65 for colour intensity, and classified fruit into low, moderate and high polyphenol levels with 71% accuracy.
Region matters. Principal component analysis showed clear geographic differences: El Jahliye fruit had low to moderate polyphenol levels, Rachiine moderate to high, and Hasbaya the full range — so harvest strategies should be region-specific. Comparing technological, phenolic and actual harvest dates gave recommended harvest windows per region.

The results were published in Scientific Reports ( ), the first study in Lebanon to use machine learning to predict food-quality indicators of pomegranates.

Perspective