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Most of the African languages are low-resourced and their linguistic characteristics are different from those of the main languages used by machine learning models. Our work focuses on providing  better representations of text and speech for these languages. I will present our approaches of features engineering (abstract syntax tree, distributional representation, ...) and representation learning (multilingual, contrastive, ...) to deal with the mentioned issues in order to improve the quality of NLP models for African languages. We propose to combine the two classes of representations to enhance the integration of syntactic and semantic properties of African languages in NLP applications. 

Finally, following this talk a session will be dedicated to discuss the integration of these methods for improving alert and surveillance systems in the agriculture domain (e.g. food security, One health, etc.).