This publication aims to raise awareness about existing AI applications in agriculture and to inspire stakeholders to develop and replicate the new ones.

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.