René Søndergaard Nilsson

Operational planning, automation, and optimisation of field operations are ways to sustain the production of food and feed. A coverage path planning method mitigating the optimisation and automation of harvest operations, characterised by capacity limitations and features derived from real world scenarios, is presented. Although prior research has developed similar methods, no such methodologies have been developed for (i) multiple field entrances as line segments, (ii) the feasibility of stationary and on-the-go unloading in the headland and main field, (iii) unloading timing independent of the full bin level of the harvester, and (iv) the transport unit operational time outside the field. To find the permutation that best minimises the costs in time and distance, an artificial bee colony (ABC) algorithm was used as a meta-heuristic optimisation method. The effectiveness of the method was analysed by generating simulated operational data and by comparing it to recorded data from seven fields ranging in size (5–26 ha) and shape. The implementation of controlled traffic farming (CTF) in the coverage path planning method, but not with the recorded data, resulted in a reduced risk of soil compaction of up to 25%, and a reduction in the in-field total travel distance of up to 15% when logistics was optimised simultaneously for two transport units. A 68% increase in the full load frequency of transporting vehicles and a 14% reduction in the total number of field to storage transports was observed. For fields located at outermost edges of the storage facility (>5 km), the increase in full load frequency, average load level, and decrease in in-field travel distance resulted in a reduction in fuel consumption by 7%. Embedding the developed coverage path planning software as a service will improve the sustainability of harvest operations including a fleet of one to many harvesting and transporting units, as the system in front of the vehicle operator calculates and displays all required actions from the operator.

Precision Farming (PF) and Controlled Traffic Farming (CTF) are well known concepts within agriculture, but the adoption rate of these practices by farmers is still very low, because farmers lack the needed skills or fail to see the benefits of using these practices. If farmers want to reap the full benefits, operational planning must be carried out in advance for the entire crop cycle, before the crop season begins. However, operational planning across the entire crop cycle is a non-trivial task, since the efficiency of each operation is determined by a range of selected operational features (e.g., wayline direction, operational speed, vehicle capacity, wayline sequence, and turn type). To that end, we present, in this paper, an application that can support farmers with operational planning of field operations with CTF, by automating the process. It provides the farmer with an overview of all his field operations, and acts as a decision support tool during the operational planning process. The application allows farmers to store and manage field and equipment information, which is used as input, when setting up CTF and generating way lines and route plans for the individual fields. One of the key benefits of the application is the provided comparison feature, where farmers can compare alternative solutions, based on Key Performance Indicators (KPIs). Results from an example field, for operations with different machine setups, are presented to illustrate how KPIs and visualisations can support farmers during the decision process.

We introduce a deviation-tolerance software architecture, which is devised for a prototype of a cloud-based harvest operation optimisation system issuing harvest plans. The deviation-tolerance architecture adapts the fault tolerance notions originating in the area of systems engineering to the harvest domain and embeds them into the Vienna developed method (VDM) model at the core of our harvest logistics system prototype. The fault tolerance supervision/execution level architecture is framed under the notion of an “harvest coach” which diagnoses deviations to the planned operations using “harvest deviation monitors” and deploys a novel “plan” (controller) that mitigates the encountered “deviation” (fault). The architecture enabled the early start of field experiments of the harvest logistics system prototype, which lead to the validation/refutation of early design stage assumptions on the diverse system components behaviours and capabilities. For instance, we casually found discrepancies in the arithmetic precision of open-source libraries used in the conversion of vehicle positioning coordinates, we assessed the maturity of the frameworks used to develop the field user interfaces, and we calibrated the level of system-operator interactivity when deviations occurs. The obtained results indicate that the architecture may have a positive impact in the context of developing systems featuring intrinsic human-driven deviations which require mitigation.