AI applications in the agricultural sector mainly focus on intensive and industrialised farming systems.

The data for these applications is generated through remote sensing technologies using satellites, planes and unmanned aerial vehicles (drones) and through on the ground sensors in combination with IoT technology.

Especially the use of unmanned aerial vehicles has allowed scaling up information collection, allowing the development of applications ranging from the identification of water stress, monitoring of crop diseases and weed mapping to crop yield prediction. AI techniques currently used in agricultural applications mainly fall under the machine learning category and include artificial neural networks and decision tree algorithms.

AI also underwrites the development of robotic applications in agriculture, for weeding, harvest etc.