# The Architecture of an Agricultural Data Aggregation and Conversion Model for Smart Farming
## 1 Introduction  
Society is facing considerable challenges in building a sustainable future in different regions of the planet, including population growth, an increasingly worse environment, changing attitudes towards food consumption, such as an increase in the consumption of animal protein, an ageing population, migration, and climate change [1, 2]. Moreover, excess nitrogen and phosphorus enter terrestrial ecosystems because of agricultural activities [3, 4]. These chemicals have toxic effects on agricultural workers, their families, and local populations, both directly and through agricultural fields. As a result, a pesticide toxicity and exposure risk index [5, 6] can be established. These effects can be mitigated with autonomous smart farming because chemicals such as fertilisers and pesticides are applied as and when needed and only to the right areas. In addition, there are several other aspects of precision agriculture that would reduce on-farm energy consumption, such as replacing heavy tractors with lightweight, autonomous vehicles that reduce soil compaction, the energy needed for tillage, and CO\({}_{2}\) emissions. Modern technologies are increasingly being deployed in agricultural machinery, creating Information and Communication Technology (ICT)-based solutions such as global-positioning-based route planning, the automatic control of implements, fertiliser and irrigation mapping, soil analysis, and crop health monitoring. Moreover, remote sensing technologies for big-data-processing challenges are being introduced into agriculture [7]. Data on agronomic workloads are collected and recorded in digitised computer format files from tractors, harvesters, and implement systems. These data are exported from the computers used in smart farming machines and imported into special software to optimisemechanised operations according to pre-determined digitised work plans. These work plans are developed and analysed in different farm management systems. However, it is not only important to buy modern agricultural machinery; it is also important to take full advantage of the opportunities offered by ICT. In most cases, farms use machinery from several manufacturers with different systems for collecting, processing, and displaying data. There are also differences in the operating systems of the onboard computers and, most importantly, in the formats of the data stored. The user is faced with the problem of having modern solutions installed in their agricultural machinery but not being able to collect, combine, and analyse their data comprehensively. This incompatibility between software and data structures leads to a loss of potential for smart solutions. Digital data provide sufficiently accurate and convenient accounting and forecasting for performance; however, the full use of all available data is often limited by the lack of uniformity in the transmission of data from different farming machines, and current standards do not cover all cases of data transmission from farming activities. This does not ensure the seamless exploitation of the potential of smart farming [8]. Moreover, according to a survey of experts in Brazil, the adoption of ICT and the application of these skills in agriculture often vary considerably across regions of the world [9]. Even the relatively widespread adoption of advanced agriculture in Germany (42-58%, according to the survey) faces a lack of compatible interfaces in agricultural technology [10]. The age of farmers and how advanced the machinery used is are still essential factors in applying ICT in agriculture [11]. Smart farming, including automation, has already developed models that increase farm productivity and quality, improve working conditions, and reduce manual labour. These improvements create sustainable farming.