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Digital agriculture in action
Improvement of capacity and tools for capturing and processing data and substantial advances in the field of machine learning open new horizons for data-driven solutions that can support decision-making, facilitate supervision and monitoring, improve the timeliness and effectiveness of safety measures (e.g. use of pesticides), and support automation of many resource-consuming tasks in agriculture.
This publication presents the reader with a collection of informative applications highlighting various ways AI is used in agriculture and offering valuable insights on the implementation process, success factors, and lessons learnt.