Robotics in precision agriculture has the potential to improve competitiveness and increase sustainability compared to current crop production methods and has become an increasingly active area of research. Tractor guidance systems for supervised navigation and implement control have reached the market, and prototypes of field robots performing precision agriculture tasks without human intervention also exist. But research in advanced cognitive perception and behaviour that is required to enable a more efficient, reliable and safe autonomy becomes increasingly demanding due to the growing software complexity. A lack of collaboration between research groups contributes to the problem. Scientific publications describe methods and results from the work, but little field robot software is released and documented for others to use. We hypothesize that a common open software platform tailored to field robots in precision agriculture will significantly decrease development time and resources required to perform experiments due to efficient reuse of existing work across projects and robot platforms. In this work we present the FroboMind software platform and evaluate the performance when applied to precision agriculture tasks.

The Unmanned Aerial Systems (UAS) legislation in the European Union will expectedly become unified by 2020. This legislation divides UAS operations into three categories, one of which is the Specific category, in which operations of higher risk will be placed. Permission for UAS operations in the Specific category is granted based on a Specific Operations Risk Assessment (SORA). The risk assessment categorizes operations into Specific Assurance and Integrity Levels (SAILs), which determines the level of the requirements for the operation such as operational procedures, training of the remote crew, UAS specification and maintenance etc. However, a conceptual gap exists between the defined technology requirements for UAS and the available UAS platforms. In this work, a prototype assessment tool based on the SORA defined technology requirements has been developed. The tool is a web-based questionnaire, which pulls the questions from a data structure and stores the answers in a database for analysis. Upon completion of the questionnaire, the user receives an automatically generated evaluation report. The evaluation report points out the areas of inadequacy and provides suggestions for mitigating these. The tool in its current state has been used to evaluate four commonly used UAS. The four UAS have been tested for SAIL II through IV, and the results show that only one of the selected UAS complies with SAIL II. Future work is to refine the tool including the list of questions and the UAS evaluation report generator, based on feedback from UAS domain experts and to expand it to cover fixed-wing UAS.

The stricter legislation within the European Union for the regulation of herbicides that are prone to leaching causes a greater economic burden on the agricultural industry through taxation. Owing to the increased economic burden, research in reducing herbicide usage has been prompted. High-resolution images from digital cameras support the studying of plant characteristics. These images can also be utilized to analyze shape and texture characteristics for weed identification. Instead of detecting weed patches, weed density can be estimated at a sub-patch level, through which even the identification of a single plant is possible. The aim of this study is to adapt the monocot and dicot coverage ratio vision (MoDiCoVi) algorithm to estimate dicotyledon leaf cover, perform grid spraying in real time, and present initial results in terms of potential herbicide savings in maize. The authors designed and executed an automated, large-scale field trial supported by the Armadillo autonomous tool carrier robot. The field trial consisted of 299 maize plots. Half of the plots (parcels) were planned with additional seeded weeds; the other half were planned with naturally occurring weeds. The in-situ evaluation showed that, compared to conventional broadcast spraying, the proposed method can reduce herbicide usage by 65% without measurable loss in biological effect.

Unmanned Aerial Systems (UAS) operations are evolving towards autonomous flight beyond visual line of sight (BVLOS), which requires moving assessments normally conducted by the remote pilot to the autonomous software. An important assessment currently conducted by the remote pilot is whether the current weather conditions pose a safety risk to the flight. This work deals with the development of a software framework for analysing weather data. The developed framework is capable of providing a weather analysis report to a remote supervisor or to an autonomous decision-making software. The software framework has been tested using historical weather data for a full calendar year provided by IBM. The weather data was applied to four different UAS ranging from small UAS to a passenger UAS. The results obtained show that during a full year, flight was possible from 53.9 % to 95.8 % of the time depending on the UAS. We consider the software framework an important step towards improving the operational safety for autonomous UAS operations under BVLOS conditions.