The Nawras project utilises artificial intelligence to assess the law's ability to protect the marine environment. It is developing a methodology that combines legal science and AI to identify indicators for measuring 'where, when and how the law protects the oceans', with artificial intelligence automating the extraction of legal information.

law, marine biology, oceanography, artificial intelligence

The project DIG-AI for Deciphering plant genotype-phenotype Interactions using knowledge Graphs and AI, aims to leverage AI to improve crop production capacities in developing countries through a better understanding of genotype-phenotype relationships in a context of climate change.

agroecology, developmental genetics in plants, artificial intelligence

This project aims to generate ecological indicators and predictive models of biodiversity in disturbed ecosystems by combining artificial intelligence methods with known assessment approaches. It will thus enable the creation of the first marine biodiversity knowledge base and, subsequently, the development of prediction and interpretation models. It also aims to reveal intelligent solutions for nature in order to ensure the sustainability of coastal socio-ecological systems.

marine biotechnology, marine ecology, biological oceanography

The AIME project utilises artificial intelligence to integrate heterogeneous data — satellite imagery, underwater images and videos, environmental DNA, GPS coordinates and telemetry — and to produce marine biodiversity indicators at different scales. The methods combine deep learning, ontologies and knowledge bases, natural language processing, machine learning and dynamic Bayesian networks, with a hybridisation of symbolic and sub-symbolic approaches aimed at enhancing the robustness and accuracy of the indicators.

marine biology, bioindicators, oceanography, artificial intelligence

"The rise of machine learning allows us to consider a completely different approach, without any physical assumptions, based solely on empirical data. The idea is to feed the algorithms with all the quantitative observations of natural phenomena that we can muster, to see if they can identify factors that have so far escaped the attention of specialists."

geophysics, geodynamics, artificial intelligence

The SMART-WING project is breaking new ground by developing a smart wing that can automatically switch between high and low altitude modes depending on the presence of animals. Thanks to its on-board artificial intelligence, the wing detects megafauna in real time, automatically descends to capture high-resolution images, and then resumes flight. This system will enable accurate data to be collected over large areas in an autonomous, efficient and environmentally friendly manner.

marine biology, biological oceanography

Predomics is a new machine learning approach designed for metagenomic data, which addresses the lack of interpretability in current predictive models for microbiome biomarkers. Inspired by interactions within microbial ecosystems, it bases its decisions on a simple score, obtained by adding, subtracting or dividing the cumulative abundances of microbiome measurements.

nutrition, environmental biotechnology, genomics

Launched in 2024 for three years and coordinated by Mounia Tahiri (Mohammed V University in Rabat), JEAI PESAS is developing a drought early-warning tool for Morocco. Machine learning models fuse satellite imagery, historical records and field measurements to forecast groundwater levels and quality several months ahead, starting with the Bouregreg basin, with support from IRD units G-EAU, IGE and UMMISCO.

hydrogeology, hydrology, machine learning, water