To increase the yields from the existing farmland to feed a growing population without a great increase in the use of resources (water, fertilization and manpower), new precision farming methods are emerging. These methods rely heavily on the detailed monitoring of assets such as crops and livestock, which is made possible with drones.

Up until now, regular use of farming drones has been limited due to the requirements for the human operator(s) of the drone and operational costs. To make this technology accessible to the average farmer, it is critical that in the near future, agricultural drones are developed with increased autonomy, robustness and flexibility in mind.

The path forward requires integration of the drones and other cyber physical systems on the farm into a common Farm Management System (FMS) to facilitate the use of big data and artificial intelligence (AI) techniques for decision support. Such a solution has been implemented in the EU project AFarCloud (Aggregated Farming in the Cloud). With the usage of cutting-edge technologies such as cloud computing, big data, and deterministic machine learning algorithms, AFarCloud intends to be the be-all and end-all solution for every farming business.

This paper explores the feasibility of autonomous drone operations in the agricultural sector from regulatory and practical points of view. It provides lessons learned from the AFarCloud EU project where drones from different manufactures were successfully integrated with a cloud-based middleware and describes the development of a subset of relevant drone-centered applications that were tested as part of the AFarCloud project.