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AI in Agriculture is a topic of crucial importance for the European Union. Several initiatives are taken in that area that is of growing importance in the context of Agriculture 4.0 and the future of farming. This section provides various entry-points on existing projects and pilots related to this topic.
This metadata document provides information about the Soil Productivity Map. This map was generated following the methodology presented in Schaetzl, R. J., Krist Jr, F. J., & Miller, B. A. (2012). A taxonomically based ordinal estimate of soil productivit...
Agriculture productivity maps based on satellite images at the field level are revolutionizing the way farmers manage their crops. These maps provide detailed insights into the health and performance of individual fields, enabling farmers to make data-dri...
Automatic detection of in-field weeds using super-resolved Sentinel-2 at 1m per pixel imagery and delineated field boundaries.
We broaden the interpretation of intermediate values of NDVI corresponding to the growing stage of a crop with the aim of helping farmers to assess the growth evolution. NVDI extracted from the imagery from the satellite Sentinel-2 for 4 years, for 11 plo...
Agriculture productivity maps based on satellite images and machine learning algorithms have become powerful tools for understanding and optimizing agricultural practices. By combining the capabilities of satellite imagery and machine learning algorithms,...
The aim of OpenDR Project is to develop a modular, open and non-proprietary toolkit for core robotic functionalities by harnessing deep learning to provide advanced perception and cognition capabilities, meeting in this way the general requirements of rob...