As society moves towards being more and more digitalised, so does agriculture as well. Hopefully these digital tools will help us reach environmental goals through optimizing the use of resources in the agricultural sector. However, there are a few barriers to overcome for the digital transition to run smoothly. This thesis investigates a small portion of the agricultural innovation network in Sweden. This is done through multiple interviews and literary research. We can clearly see how this innovation network during the last decades has moved towards becoming more knowledge centered and how the model triple helix now easily can be applied to the network. The network is then analysed using Ron Boschma’s five proximity factors and it becomes clear that concerning the agricultural innovation network, the cognitive and institutional proximity is of great importance for the innovational strength. It also becomes clear that the different proximity factors seldom act alone, but instead build upon each other to increase the negative respectively positive outcomes. Moreover it is shown that regarding the agricultural innovation network in Sweden, it can only benefit from improving the proximity factors, which is different from other types of industries.

Artificial Intelligence (AI) is increasingly used in different parts of society for providing decision support in various activities. The agricultural sector is anticipated to benefit from an increased usage of AI and smart devices, a concept called smart farming technologies. Since the agricultural sector faces several simultaneous challenges, such as shrinking marginals, complicated pan-European regulations, and demands to mitigate the environmental footprint, there are great expectations that smart farming will benefit both individual farmers and industry stakeholders. However, most previous research focuses only on a small set of characteristics for implementing and optimising specific smart farming technologies, without considering all possible aspects and effects. This thesis investigates both technical and non-technical opportunities and hurdles when implementing AI in Swedish agricultural businesses. Three sectors in agriculture are scrutinized: arable farming, milk production and beef production. As a foundation for the thesis, a literature review revises former research on smart farming. Thereafter, an interview study with 27 respondents both explores the susceptibility and maturity of smart farming technologies and provides examples of technical requirements of three chosen applications of AI in agriculture. Findings of the study include a diverse set of aspects that both enable and obstruct the transition. Main identified opportunities are the importance smart farming has on the strategic agendas of several industry stakeholders, the general trend towards software technology as a service through shared machinery, the vast amount of existing data, and the large interest from farmers towards new technology. Contrasting, the thesis identifies main hurdles as technical and legislative challenges to data ownership, potential cybersecurity threats, the need for a well-articulated business case, and the sometimes lacking technical knowledge within the sector. The thesis concludes that the macro trend points towards a smart farming transition but that the speed of the transformation will depend on the resolutions for the identified obstacles.

Internet of Things (IoT) and Machine Type Communication (MTC) have got more momentum in the last few years but still, need to be penetrated with their full swing in our daily life. This can be possible with general framework that provides global network coverage. Non-terrestrial networks comprised of satellites and aerial platforms are expected to provide next-generation communication services in underserved and un-served areas by ensuring the quality of service that cannot be covered by existing terrestrial networks owing to economical and geographical limitations. The aim of this thesis is to formulate a set of massive and critical MTC use cases such as global environment monitoring, tracking of shipping containers and smart agriculture, and assess their comprehensive requirements like data size, sensor node density and uplink capacity and discuss possible network architectures and deployments focusing on satellite or aerial networks. A rigorous discussion on different network architectures to address the requirements have been presented, that involve (1) Low Earth Orbit (LEO) satellite based network, (2) High Altitude Platform (HAP) based network, and (3) HAP and UAV based network. The proposed network architectures have been simulated and analyzed using MATLAB tools for respective use cases in terms of required number of satellites or aerial platforms. The criteria for selection of network architectures for the use cases are based on the minimum number of satellites or aerial platforms. The results show that LEO constellation consisting of 260 satellites are feasible concerning deployment and management for global environment monitoring network. Similarly, 1440 LEO satellites provide global coverage for tracking of shipping containers. Smart agriculture use case requires high throughput, and hence HAP and UAV integrated network architecture is more realistic for a fully autonomous system as compared to other network architectures. Cooperative control and management of set of agricultural machines can be performed at the UAV. Simulation results show that single UAV can be capable of commanding and controlling the agricultural smart machines in one square kilometer crop field and can send the summary of events to the central station via a HAP. / Internet of Things (IoT) och maskintypkommunikation (MTC) har fått mer fart under de senaste åren men måste fortfarande penetreras med sin fulla sväng i vårt dagliga liv. Detta kan vara möjligt med allmän ramverk som ger global nätverkstäckning. Icke- markbundna nät bestående av satelliter och flygplattformar förväntas tillhandahålla nästa generations kommunikationstjänster i undervärdiga och obetjänade områden genom att säkerställa kvaliteten påtjänster som inte kan täckas av befintliga marknät pågrund av ekonomiska och geografiska begränsningar. Syftet med den här avhandlingen är att formulera en uppsättning massiva och kritiska MTC-användningsfall som global miljöövervakning, spårning av fraktcontainrar och smart jordbruk, och utvärdera deras omfattande krav som datastorlek, sensornoddensitet och upplänkkapacitet och diskutera möjliga nätverk arkitekturer och distributioner med fokus påsatellit- eller flygnät. En rigorös diskussion om olika nätverksarkitekturer för att möta kraven har presenterats, som involverar (1) Low Earth Orbit (LEO) satellitbaserat nätverk, (2) High Altitude Platform (HAP) baserat nätverk, och (3) HAP och UAV baserat nätverk. De föreslagna nätverksarkitekturerna har simulerats och analyserats med MATLAB-verktyg för respek- tive användningsfall i termer av det nödvändiga antalet satelliter eller flygplattformar. Kriterierna för val av nätverksarkitekturer för användningsfallen är baserade pådet minsta antalet satelliter eller flygplattformar. Resultaten visar att LEO-konstellationen bestående av 260 satelliter är möjlig när det gäller distribution och hantering för globalt miljöövervakningsnätverk. Påliknande sätt ger 1440 LEO-satelliter global täckning för spårning av fraktcontainrar. Småjordbruksanvändningsfall kräver hög kapacitet, och följaktligen är HAP och UAV integrerad nätverksarkitektur mer realistisk för ett helt autonomt system jämfört med andra nätverksarkitekturer. Kooperativ kontroll och hantering av jordbruksmaskiner kan utföras vid UAV. Simuleringsresultat visar att en enda UAV kan vara kapabel att kommandera och kontrollera jordbrukssmarta maskiner i ett kvadratkilometer grödningsfält och kan skicka sammanfattningen av händelser till centralstationen via HAP.