oNe hEalth SusTainabiLity partnership between EU-AFRICA for food sEcuRity
NESTLER is a joint EU-Africa project utilizing AI, IoT, and remote sensing to establish a One-Health sustainable partnership. We holistically monitor the well-being of animals, plants, and humans, translating data into predictive models. The project promotes a shift to a circular economy by integrating sustainable practices like edible insect farming and evaluating their economic viability, leading to a joint task force on sustainability.
NESTLER is a joint EU-Africa project utilizing AI, IoT, and remote sensing to establish a One-Health sustainable partnership. We holistically monitor the well-being of animals, plants, and humans, translating data into predictive models. The project promotes a shift to a circular economy by integrating sustainable practices like edible insect farming and evaluating their economic viability, leading to a joint task force on sustainability.
Wild Animal Recognition Video Dataset
This Data set contains videos of four animal classes, namely Foxes, Jackals, Ravens and Vultures, captured at RAKOVO, Silven region, Bulgaria. The dataset also contains videos of 12 classes of wild animals that can be found in Africa. Namely, the 12 classes are Baboons, Buffaloes, Elephant, Giraffes, Gorillas, Hippopotamus, Impala, Lions, Rhinocerus, Topi, Warthog, Zebra, captured at Rwanda and Uganda. The folder “Elephants-Gorillas-Kobs_Detection_Dataset” contains the extracted frames which are accompanied by their respective bounding box annotations making this part of the dataset suitable for detection tasks. The videos were captured in Uganda at different periods of 2025 (January, February, June) using a HD camera (1920×1080) from CTPH.
Poultry Video Dataset
The dataset contains several videos of poultry while eating, drinking water and moving around into their hen house.
The dataset can be useful to model the behaviour of the poultry using for example optical flow methods.
The videos were captured using RGB cameras which were fixed and mounted in a way to stand over the poultry.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 832981
