## 2 Characteristics of the Sectors in Focus
### Smart Agriculture  
Smart Agriculture (SmA), also known as Agriculture 4.0 by analogy to Industry 4.0, is based on the earlier concept of Precision Agriculture (PrA), referred to as Agriculture 3.0, but armed with the latest ICT technologies. PrA is based on taking into account the high spatial variability of agricultural phenomena on careful local observation with high-resolution images and ensuring an appropriate local response in contrast to the previous approach (which is still commonly used) in which the average properties of the arable land or crop and uniform agotechnical treatments are considered. The mentioned local phenomena include the arable land properties (soil type/abundance/reaction, terrain slope/exposure, water conditions, microclimate, the influence of the neighborhood, etc. ), conditions of the crop (stage of plant development, degree of plant nutrition, presence of pests or weeds, infection or infestation symptoms, damage from harsh weather conditions or wildlife), and the yield obtained. In response to the above, the agotechnical treatments may be locally adjusted, e.g., sowing rate, doses of fertilizers and pesticides, etc. [9]. The main difference between PrA and SmA is the integration of machines and devices (which, thanks to electronic solutions, are becoming more and more intelligent) and Information Systems (ISs) powered with AI--especially Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), or Supervisory Control and Data Acquisition (SCADA) systems adapted specifically for agriculture--into one big system that allows data acquisition, storage, and processing and is also used for the continuous monitoring, controlling, and automating of agricultural production processes and making production and business decisions. Since all components in the technical system of SmA are continuously interconnected using wireless communication technologies to provide RT data exchange and form a Monitor-Analyze-Plan-Execute based on Knowledge (MAPE-K) [10] chain, a necessarily quick, multiple factor-based and optimized response to occurring phenomena is possible, which prevents, e.g., losses due to infestation spread or the impact of the temporary underdevelopment of a crop on the yield. Furthermore, SmA support systems can also effectively reduce operating costs by optimizing the use of equipment, transportation routes, and field passages during agricultural treatments (tillage/harrowing, sowing, fertilizers/pesticides application, harvesting) based on arable land geometry, mechanical properties, etc. The first attempts to use robots in agriculture are taking place, but widespread robotization in agriculture is expected during the fifth agricultural revolution to come (Agriculture 5.0). The concept of SmA may be adapted to other kinds of farming, e.g., livestock production or fish farms [11]. The inherent foundation of agriculture from the third generation onward is the use of Geographic Information Systems (GISs). The descriptions of the arable land properties and parameters of crop conditions as spatially conditioned are tagged with geographic coordinates and then processed in the spatial aspect. This also applies to all analytics and treatment parameters (doses of means of production) that are determined to be used. The history of readings, agotechnical procedures, and harvested crops is also stored in a geo-spatial context. For this to be possible, all devices that perform monitoring and execution processes in the MAPE-K chain (i.e., broadly understood sensors and effectors) must be equipped with devices for reading positions instantaneously with a required accuracy of several centimeters. In turn, the MAPE-K analysis and planning phases are supported by Spatial Decision Support Systems (SDSSs) on top of a GIS underlay.