AgriDataValue aims to establish itself as the “Game Changer” in Smart Farming digital transformation and agri-environmental monitoring, and strengthen the smart-farming capacities, competitiveness and fair income by introducing an innovative, open source, intelligent and multi-technology, fully distributed Agri-Environment Data Space (ADS). To achieve technological maturity, fast and massive acceptance, AgriDataValue adopts and adapts a multidimensional approach that combines state of the art big data and data-spaces’ technologies (BDVA/ IDSA/ GAIA-X) with agricultural knowledge, monetization, new business models and agri-environment policies, leverages on existing platforms, edge computing and network/ services, and introduces novel concepts, methods, tools, pilot facilities and engagement campaigns to go beyond today’s state of the art, perform breakthrough research and create sustainable innovation in upscaling (real-time) agricultural sensor data, already evident within the project lifetime. -------------------------------------------------------------------------------------------------------------------------------------------- This dataset contains images along with their bounding box annotations for the purpose of weed detection. The bounding box annotations are in Yolo format and include two classes 'Crop' and 'Weed'. The images were captured by a UAV over a field seeded with celeriac. The resolution of the images is 5280(width) x 3956(height). Due to images' high resolution, image tiling and patching is encouraged (e.g. to 640x480) to also increase the number of images. Train/test/validation splitting was done in a random manner.