## 3 Use Cases
The observation of plants at high resolution (not only the colors of the visible spectrum but also the way they are distributed on a plant's surface) allows for the determination of their nutritional status, deficiencies of individual nutrients, and identification of infections or infestations. The assessment can be performed in either active (stimulated reflection) or passive mode (reflection of solar radiation). The former, being daylight condition-independent, is used in low-distance applications (handheld equipment or equipment mounted on agricultural treatment machinery, e.g., on booms of a tractor or cultivation set) to provide instant data that can be used for an immediate local calculation for a relevant response, e.g., a dose to be applied (both readouts and responses are then recorded with a time-spatial stamp). The latter is mainly used in photogrammetry with the assistance of diverse flying objects, among which drones are of importance due to their high achievable resolution (on the order of a single centimeter or less per pixel). Image acquisition is carried out by a set of narrow-band sensors in a specialized camera or RGB visible light channels and the IR channel in simplified solutions. The sets of band images with the necessary high terrain overlap are combined into orthophotmaps and further analyzed with specialized software to produce a map of a specific phenomenon's intensity using various algorithms with different and complementary properties, which is a highly computationally complex process that cannot be performed onsite. Additionally, this technique can be used for field mapping to detect heavy and light mineral soil or peat soil. In SmA, the use of drones to create orthophotmaps could, in the future, be relatively frequent, at least several times during a growing season. In particular, if an infection or contamination is detected, the rapid assessment of its extent and the immediate action that needs to be taken will be important to prevent the further spread of the phenomenon and minimize the use of pesticides, which will help to reduce losses and production costs and minimize the negative impact on the environment. Therefore, it will be necessary to transmit very-high-volume images from the drone to the SmA IS quickly, without limiting the flight speed for the drone buffer memory overflow avoidance in order to start the data processing as soon as possible and to provide feedback to the staff and equipment waiting onsite. **UC4 Remote sensing using Light Detection and Ranging (LiDAR):** The LiDAR technique allows precise distance measurements of objects based on the laser impulse propagation time or changes in the wavelength of light. Typically, there are two types of LiDAR sensors: passive, which use measurements based on the reflected sun rays, and active, which utilize their own light source to perform measurements. In the AFBW sectors, the LiDAR technique is mostly used for the evaluation of spatial variability and surface modeling (e.g., creating digital elevation models of catchment/sub-catchment boundaries [47], flood risk maps [48], and the evaluation of forest yields [49]) or the identification of the spatial and specific spectral properties of objects (evaluation of soil composition [50, 51], classification of trees [52], forest inventory, 3D modeling of trees [53], and many more). In both cases, LiDAR imaging involves the acquisition of high-volume images, typically on the order of gigabytes (hundreds of megabytes per image file), with the size dependent on the image resolution, applied compression, etc. [54]. In addition, LiDAR sensors are applied to both ground-level and in-air operations, which can imply the need to meet additional transmission requirements depending on the storage and processing capabilities provided onboard a flying vehicle (e.g., UAVs). It has to be noted that both UC4 and UC3 are similar in terms of communication requirements, with the exception of the data processing approach.