AgriAdapt introduces a novel dynamic resource-efficient machine learning technology to the domain of unmanned aerial vehicle (UAV)-based precision agriculture. Within this project, the University of Ljubljana (UL) will transfer its context-aware neural network adaptation framework to Geo-K to be used for on-UAV weed recognition from aerial images. The ULs framework allows dynamic slimming of the neural network so that the memory and computational burden of the network adapts to the problem at hand. This translates to energy savings, as the amount of computation on the UAV is reduced in real-time with negligible loss of inference accuracy. AgriAdapt will bring a competitive advantage to Geo-K who will be able to provide precision agriculture solutions using cheaper UAVs without hurting the UAV flight duration. To the University of Ljubljana, the project will enable technology validation that will be leveraged for future commercialization of the framework.