# The Architecture of an Agricultural Data Aggregation and Conversion Model for Smart Farming
## 2 Review  
As reported in the European Parliament's Science and Technology Options Assessment (STOA), smart farming technologies make it possible to produce \"more with less\" [12]. The use of natural resources, agrochemicals, antibiotics, and energy will be reduced for the benefit of farmers, the environment, and society. In this respect, it is assumed that a comprehensive and systematic approach achieved via intelligent sustainable and digital automated production is urgently needed. From this perspective, precision agriculture needs to consider the use of various applications such as data collection and cloud computing, cyber-physical systems management, decision support systems, and autonomous vehicles in agricultural processes. These solutions aim to establish a holistic approach in which everything is closely linked to the optimisation of production, energy, water, food quality, and services. ICT makes it possible to carry out tasks more efficiently, quickly, and comfortably for the operator and to use digital working data for farm record-keeping and the implementation of advanced farming. The conventional technologies utilised here are continuously variable transmissions, auxiliary guidance systems and power units, alternative fuels, remote diagnostics, controllers operated via specific communication protocols, data acquisition and transmission units, fleet information systems, etc. [13]. Different data exchange models have been applied in individual agricultural modernisation projects [14]. FLOURISH (H2020-ICT) includes a three-dimensional multispectral map with high temporal and spatial resolutions, land intervention tools and techniques, data analysis tools for crop monitoring, weed detection, and a user interface designed to facilitate agricultural decision making. EU-PLF (FP7-KBBE) developed management tools for the continuous, automated, real-time monitoring of animal welfare, health, environmental impacts, and production. PIGWISE (Eranet ICT-AGRI) developed an individual pig performance and welfare monitoring tool that monitors warning signs such as changes in animal behaviour and other parameters for the early detection of diseases. RoboFarm (EU ICT-AGRI ERA-NET) aims to develop a technology platform that integrates and harmonises existing software and hardware technologies into a single system, using robots equipped with sensors and active vision systems capable of collecting data from the field and transmitting it to farmers [15]. One of the first initiatives to unify the exchange of data from agricultural machinery was the iGreen project, which involved research institutes, government agencies, Original Equipment Manufacturers (OEMs), and service providers [16]. In this dynamic, network-based architecture, the raw value from each CAN-Bus message was stored as a JavaScript Object Notation (JSON) document. Although the platform was successful in practice tests, the final communication with the data server occurred through the same central agricultural machine. This showed the platform's potential but also the limitations of its use. A cloud-based farm-data-management architecture makes the use of the farmer's own and external information convenient and easily accessible via Internet connection only, including the functionality of a social network for farmers [17]. This architecture does not cover initial data collection for different agricultural machines; however, a JSON format for server messages was used. The platform architecture of the AFarCloud project is defined and consists of three main functional components: (a) the farm management system, (b) the semantic middleware, and (c) the hardware [14]. The platform is connected to other external data sources, such as third-party data and legacy system databases. The farm management system offers a task management tool for planning collaborative tasks involving unmanned aerial vehicles (UAVs) and ground vehicles (i.e., tractors).