## 3 Use Cases
Typically, LiDAR applications do not require near-real-time data transfer and processing to facilitate real-time decision-making processes (e.g., determining the dose of insecticide or fertilizer to be applied to a plant) as opposed to UC3. **UCS Yield mapping:** This is used for the evaluation of agrotechnical treatment efficiency throughout a growing season. The yield and harvesting process parameters depend on the harvested crop and the design of the combine harvester, which contains multiple onboard sensors providing geo-spatially tagged readouts with a 1-5 s resolution [41, 42]. The need to continuously transmit yield monitoring data is dependent on the farm size and the requirements of its management model (e.g., centralized RT farm activity monitoring). **UC6 Stationary or quasi-stationary sensing:** The monitoring of information from stationary or quasi-stationary sensors for various purposes will, in the future, be common in all the AFBW sectors. In SmA, permanently installed sensors can provide insights into the local situation in distant arable lands, e.g., weather stations or soil moisture sensors. In the case of SmF, in order to track forest growth and health, the sensors can be used to monitor environmental parameters, such as air temperature and relative humidity, carbon monoxide and dioxide, wind speed, rain, insolation (illuminance), soil moisture, and temperature, as well as abrupt changes for fire monitoring and prediction parameters such as illuminance (both visible and IR spectra), temperature, and humidity [55]. The specialized sensors can be installed in specific equipment, e.g., pest traps or hidden magnetic sensors to detect the presence of poachers. Environmental monitoring may include the above parameters, as well as air quality indicators. Monitoring in water management [56; 57] can include both readings of surface water parameters, e.g., monitoring stations installed in rivers, where parameters such as flow, temperature, oxygenation, reaction, and the content of specific compounds (especially contaminants), and readings from a network of piezometers to monitor groundwater parameters. In smart water grids, the sensors can be used to measure water flow, volume, and losses, as well as water quality. An example of quasi-stationary sensing is the monitoring of vital signs, such as temperature, heart rate, etc., of free-grazing animals in pastures. The data sources' activity patterns can be application dependent. The extreme will be, on the one hand, sources with a discontinuous and cyclical daily activity pattern, relatively low data rate and volume, sensitivity to delays, and need for retransmission, and, on the other hand, sources requiring highly reliable RT transmission on demand (e.g., fire-alerting sensors). Moreover, sensor networks are usually expected to operate for very long periods of time without the need to charge separate devices. Therefore, it is substantial to incorporate mechanisms that enable data exchange with minimal power input to extend batteries' lifetimes. **UC7 Mobile object tracking:** The class of object tracking use cases relates to all AFBW sectors and all fields and includes the monitoring of the position and status of vehicles and specialist machinery that perform agotechnical treatments or work in the forest, wild animals wearing special wildlife collars [32], timber batches or other raw products of the forest industry, and, finally, forestry personnel or those involved in biodiversity monitoring, including national park rangers, forestry and fisheries guards, etc. For the latter, the safety and security aspect is of premium importance because these are jobs associated with a high risk of severe accidents, as well as threats from people who commit timber theft, illegal logging, or poaching.