Projects

S@founet: Screening for intra-specific diversity via an automated image-based classification approach, application to a tropical fruit tree species (2020-2022) - Funded by Agropolis Fondation (France)

Project leader: J Duminil

Scientists involved from our team : J Duminil

Indigenous fruit trees (IFT) are socio-economically important for populations from rural developing countries. The diversity of IFT has been shaped by human during their cultivation/domestication history. This has provided a tremendous diversity of morphological varieties that can now be found in farmers’ agroforestry fields, home gardens or even in the cities. Characterizing such a level of diversity at large spatial scales and in sufficient details is particularly challenging using traditional morphological approaches (direct measures in the field). The use of automated and fast- characterization systems based on image acquisition through digital cameras and state-of-the-art deep learning models are very promising in this respect. The Pl@ntNet platform has implemented such an approach with great success. Here we propose a challenging extension of this method for the study of biodiversity at an intra-specific level. The new method will be experimented on one of the most important IFT from the Congo Basin, the African plum tree (Dacryodes edulis, Burseraceae). The experimentation of this new method is very promising to characterize, monitor, and identify varieties of IFT at large geographical and taxonomical scales by relying on citizen science approaches.