Emerging precision agriculture techniques rely on the frequent collection of high-quality data which can be acquired efficiently by unmanned aerial systems (UAS). The main obstacle for wider adoption of this technology is related to UAS operational costs. The path forward requires a high degree of autonomy and integration of the UAS 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). The regulation of UAS operations is another important factor that impacts the adoption rate of agricultural UAS. An analysis of the new European UAS regulations relevant for autonomous operation is included. Autonomous UAS operation through the AFarCloud FMS solution has been demonstrated at several test farms in multiple European countries. Novel applications have been developed, such as the retrieval of data from remote field sensors using UAS and in situ measurements using dedicated UAS payloads designed for physical contact with the environment. The main findings include that (1) autonomous UAS operation in the agricultural sector is feasible once the regulations allow this; (2) the UAS should be integrated with the FMS and include autonomous data processing and charging functionality to offer a practical solution; and (3) several applications beyond just asset monitoring are relevant for the UAS and will help to justify the cost of this equipment.

Over the past decade, Unmanned Aerial Systems (UAS) have emerged as pivotal tools in fault localization inspections and scheduled assessments of overhead power lines. Driven by technology advancements and the promise of safer and more efficient inspections, the adoption of UAS among electrical grid operators has witnessed significant growth. This paper examines the associated operational aspects through structured interviews with six major grid operators in Norway. The study delves into current operational approaches, applications, data handling practices, and prospective developments of aerial drone usage. Based on the interviews, we identify key challenges that hinder the widespread adoption of drone technology in grid inspection. These challenges include the efficient distribution and planning of inspection platforms, ensuring the acquisition of consistently repeatable and comparable datasets, and the need for a fully autonomous data collection and analysis pipeline tailored for electrical grid inspections. The potential of autonomous drones to facilitate repeatable data collection campaigns, coupled with automated data analysis solutions like image recognition software, is highly desirable among respondents, offering the promise of significant cost reduction. Associated societal benefits, e.g., efficiently ensuring the reliability of ageing infrastructure, underscore the importance of further exploring and using drone technology in grid inspection programs.