Title: AI in remote sensing data for agriculture

Summary: The recent progress in deep learning is slowly but consistently shifting the paradigm that was followed in remote sensing data processing over the last decades. AI applications are starting to become available for a wide range of applications, from on-board data reduction to fine-grained analytics. Agriculture is one of the areas with the largest social impact of this technological revolution, through a two-pronged strategy. Firstly, AI technology is used to replace the typical flat-rate applications of chemicals with targeted applications only to regions of need, thus alleviating the environmental stress caused by intensive farming practices. Secondly, AI is used to substitute agronomic input across the season, something particularly meaningful in parts of the world where access to agronomical input is limited. In this talk, we are going to present in detail the related topics, while also discussing the technology bottlenecks, limitations and future directions.