Data-driven agriculture and Internet of Farming (IoF) require reliable communication systems. Nowadays, only some of the key use cases demanded by the agricultural industry verticals get support from multiple state of the art wireless technologies such as 4G, Wi-Fi, or Low Power Wide Area Network (LPWAN) technologies, combined with satellite and cloud access. However, the ones demanding very high data rates or very low latency are still not feasible. With 5G, designed for flexible support of Extreme Mobile Broadband (xMBB), Massive Machine-Type Communications (mMTC) and Ultra-reliable Machine-Type Communications (uMTC), more agricultural use cases will be possible. This paper provides a reference list of data-driven agriculture scenarios and use cases with their associated communication requirements, and whose feasibility is evaluated in a live 5G trial performed in a representative rural area scenario in the south of Denmark. The paper details a reference methodology for assessing 5G Quality of Service (QoS), including multi-connectivity schemes and reports the empirical 5G performance results, which are put in perspective of the requirements for the different IoF reference scenarios. The empirical results indicate that early 5G deployments are already capable of reliably serving data-driven agriculture vertical use cases such as those related to agricultural logistics or configuration of machinery and diagnostics in 65.8-99% of the cases; but it will be necessary to wait for 5G network upgrades and coming 5G Releases in order to operate the more low latency demanding use cases.

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.

Advanced systems for manned and/or agricultural vehicles—such as systems for auto-steering, navigation-adding, and autonomous route planning—require new capabilities in terms of the internal representation for the autonomous system of the working space; that is, the generation of a metric map that provides by numerical parameters any operation-related entity of the working space. In this paper, a real-time approach was developed for the generation of the field metric map, based on a row generation method (polygons-based geometry). The approach can deal with fields with or without in-field obstacles, where the generated field-work tracks can be either straight or curved. The functionality of the approach was demonstrated on 12 fields with different number of obstacles ranging from one to six. The test results showed that the computational times were in the range of 0.26–24.51 s. The presented tool brings a number of advancements on the process of generating a metric map for arable farming field operations, including the real-time generation feature, the potential to deal with multiple-obstacle areas, and the reduction in the overlapped area.

Agricultural injuries are a valuable social sustainability indicator. However, current methods use sector-scale production data, so are unable to assess the impact of changes in individual farming practices. Here, we developed a method that adopts a life cycle approach to quantify the number of serious injuries during agricultural production processes and assess the potential impact of changes in agricultural practices. The method disaggregates agricultural production into operations and estimates the contribution each operation makes to the frequency of different types of injuries. The method was tested using data collected by survey during an expert workshop in which sixteen participants were asked to estimate the parameters related to typical dairy cattle and pig farms. Parameter estimates for specific operations varied considerably between participants, so normalized values were used to disaggregate sector-scale statistics to production operations. The results were in general agreement with the results from other studies. Participants found it challenging to quantify the potential effect of new technologies. Provided suitable empirical statistical data are available, the method can be used to quantify the risk of injury associated with individual products and provide an ex-ante assessment of future developments in farming practices.

During a baling operation, the operator of the baler should decide when and where to drop the bales in the field to facilitate later retrieval of the bales for transport out of the field. Manually determining the time and place to drop a bale creates extra workload on the operator and may not result in the optimum drop location for the subsequent front loader and transport unit. Therefore, there is a need for a tool that can support operators during this decision process. The key objective of this study is to find the optimal traversal sequence of fieldwork tracks to be followed by the baler and bale retriever to minimize the non-working driving distance in the field. Two optimization processes are considered for this problem. Firstly, finding the optimal sequence of fieldwork tracks considering the constraints of the problem such as the capacity of the baler and the straw yield map of the field. Secondly, finding the optimal location and number of bales to drop in the field. A simulation model is developed to calculate all the non-productive traversal distances by baler and bale retrieval in the field. In a case study, the collected positional and temporal data from the baling process related to a sample field were considered. The output of the simulation model was compared with the conventional method applied by the operators. The results show that application of the proposed method can increase efficiency by 12.9% in comparison with the conventional method with edited data where the random movements (due to re-baling, turns in the middle of the swath, reversing, etc.) were removed from the data set.

With the structural transformation of agriculture, it is getting increasingly important to consider the readiness of arable land when planning and scheduling machine operations. This is to ensure that the operations produce positive results to crops and soil, while not causing damage to soil. Taking the readiness of arable land parameters into account paves the ground for executing field operations at opportune times. Determining the readiness of arable land is widely affected by stochastic factors such as soil water content and meteorological information and some variables associated with trafficability, workability, and completion criteria along with soil characteristics. This paper presents the development of stochastic optimization and statistical learning models implemented as a decision model that assists the farmers in optimal timing of tillage operations on a day-to-day basis. The optimization model is a finite horizon Markov decision process modelling execution of tillage operations at opportune time based on information acquired from a mechanistic model simulation of agricultural fields and historical and forecast meteorological data. Moreover, a Gaussian state space model predicts the future soil water content of the field using a Bayesian updating approach, which is embedded into the optimization model. Numerical examples are given to show the functionality of the model. The prediction of optimal timing of two operations, tillage and seeding, has been well aligned with the practical experiences of the selected arable land.