## 2 Related Works  
A state-of-the-art review is outlined in this section with a highlight of major applications of coupling IoT with UAV technology in smart farming. Communication technologies, network functionalities, and connectivity requirements of such integration are covered in this section. A related work windup is presented in Table 1, before using this review to highlight the research gaps and report own research motivations. [PERSON] et al. [26] proposed a low-cost Long-Range Wide-Area Network (LoRa)-based IoT platform for Smart Farming Modular IoT Architecture called LoRaFarM that aims to improve generic farms management in a highly customizable way. The proposed LoRaFarM platform has been evaluated in a real farm in Italy, where it collected environmental data (air, soil, temperature, and humidity) related to the growth of farm products (e.g., grapes and greenhouse vegetables) over a period of three months. A web-based visualization tool for the collected data is also presented, to validate the LoRaFarM architecture. Authors in [27] developed a hierarchical smart farming structure based on the collaboration between UAVs and federated Wireless Sensor Networks (WSNs) for crop monitoring. Two important aspects of such collaboration have been considered: designing the UAV trajectories for efficient data collection and implementing effective data processing algorithms (consensus and symbolic aggregate approximation) at the network level to assess relevant data transmission. Experiments were carried out at a Romanian research institute where  
Figure 2: Different types of agricultural UAVs. different crops and methods are developed. The results demonstrate that the collaborative UAV-WSN-IoT approach increases the performances in both smart farming and ecological agriculture. A smart agriculture management system (SAMS) was introduced in [28], which proposed an automated system to help farmers to increase crop production by crashing into natural resources. The system used different sensors to collect data required for the growth of the crop, where these data were fed into the ThingSpeak IoT cloud platform for data visualization. The real-time data were stored on Firebase Database and then used for crop prediction using Support Vector Machine (SVM) classifier. Researchers in [29] presented an intelligent antifrost irrigation management system that contains a climatological station and ecological anti-disaster frost irrigation. The solar-powered system interacts with the environmental system through a website, allowing the real-time interconnection, acquisition, and monitoring of information through mobile phone systems (GSM/GPRS) and internet (TCP/IP) services. Additionally, the proposed system uses an intelligent Fuzzy Expert System (FES) to predict the greenhouse temperature and control activation of a water pump, considering humidity, the temperature of inside and outside air, solar radiation, and wind speed. The intelligent model gives a prediction by determining coefficients with the analysis of variance method. [PERSON] et al. in [30] developed an application for water irrigation called \"Agrinex.\" This system is an alternative approach in collecting and measuring physical parameters from the environment, particularly in smart irrigation systems. Agrinex is connected to several in-field sensors such as a water level sensor, paddy temperature sensor, and a field weather station. This work aimed to make this information accessible anywhere wherever there is internet access using a mobile application, which makes Agrinex scalable and adaptable to changes. The mesh-based network recorded a maximum transmission distance of 11 m from sink to sensor node with a 90% success rate. Data from the field can be accessed remotely close to real-time when short network delays are accounted for. It also gives the additional benefit of equipping lands with an automated irrigation system that conserves up to 81% of water consumption.