## 2 Related Works
Authors in [31] proposed a system that decides whether or not water is needed for farms by predicting the rainfall using the Genetic Algorithm (GA) approach. In the proposed system, voluminous weather and soil moisture data are stored inside the cloud servers. Based on data collected by a UAV and then fed to the GA, water spraying is performed, which can be monitored using a mobile application. Cloud servers are used for storing and processing large volumes of weather and soil moisture data. Results show that the sensor-based system is activated to check whether the GA-based system completes its prediction correctly or not by sensing moisture levels from the soil. If the moisture level of the soil crosses the predefined threshold value, then plant watering is performed by quadrotor UAV. Researchers in [32] developed a framework for monitoring pests and diseases of crops based on IoT and UAV that attempts to solve the correlation between the occurrence of pests/diseases and weather parameters. The proposed framework is deployed in the Yangtze River Zone of China, where it uses rotary devices based on sun perception to obtain solar energy. Results infer that low-altitude remote sensing and monitoring through aerial imaging of pests and diseases using UAV is reasonable, while more analyses of climate changes would provide some precautions in advance. A Narrow Band IoT (NB-IoT) system is proposed in [33] to collect underground soil parameters in potato crops using a UAV network. Around 2500 sensors deployed under and above ground are connected to UAV using Low-Power Wireless Personal Area Network (LPWPAN). Simulation results show that due to UAV altitude and path loss, the link quality between the ground sensor and UAV is reduced. Another observation is that the lifetime of sensor batteries was varied depending on the location of the sensor under or above ground. Using LoRa technology and proper path loss model are recommended as future work to enhance link accuracy and performance. Regarding path loss, authors in [22; 23; 24; 25]discussed different types of propagation path loss from an aerial platform perspective, where Free-Space and Air-to-Ground are typical deterministic propagation models, while Hata and Okumura are typical empirical ones. Results show that an appropriate model can be selected based on coverage range, adaption across different terrains, and better Quality of Service (QoS). Authors in [34] suggested a heterogeneous IoT sensor node system to sense acoustic, rain, wind, light, temperature, and pH levels of the cornfields for smart agriculture applications. The system aimed to achieve productive corn harvest in large-scale fields using a drone that gathers data and sends it to a gateway. The simulation results that monitored at the gateway using the IoT application called Grafana show that it offers maximum efficiency from the soil and follows the crop development according to the weather and soil conditions, in addition to reducing the workload and the risk of disease and pest, as well as optimizing irrigation and obtaining better quality products at low cost. As future work, collecting a wider range of climatic data and geographical conditions would be useful in designing a model of smart farming. A related work windup in smart farming based on the IoT and UAV is presented in Table 1, before using this review to highlight the research gaps and report the authors' research motivations.