# Auto-Encoder Learning-Based UAV Communications for Livestock Management
### Related Works  
Beyond using multirotor UAVs for aerial surveillance, substantial research has been performed on UAVs to ease livestock and agricultural farming [15; 16; 17]. Drones have explicitly become helpful in monitoring and enhancing crop and livestock production due to real-time data that help farmers respond more quickly to weed incursion, pest infestation, output projections, livestock health conditions and other issues [18]. UAVs and other related technologies, such as the Internet of things (IoT), have been extensively used for smart agriculture and animal farming [9; 19; 20; 21; 22]. Different types of UAVs are applied in pest control, crop irrigation, animal health monitoring, animal rearing and other agriculture-related activities [19]. The joint application that both IoT and UAVs can play in smart-driven agriculture was also discussed in [20]. [PERSON] et al. [9] have explored the architecture, adaption and usage of UAVs for smart agriculture. The authors highlighted the UAV's applications and related technologies to  
Figure 1: Conceptual framework of UAV-based farm monitoring system [13]. efficiently enhance and optimize diverse agricultural processes using smart Bluetooth-enabled sensors. However, reliable data transfer was the major drawback of this approach due to the short range of the Bluetooth UAV-enabled system. In some scenarios, UAVs could be used as tools for mechanized agriculture to ameliorate disorders in various fields through commercial, scientific, agricultural and livestock enhancement [21]. Specifically, the paper focused on providing details of mechanized agriculture using UAV systems for pesticides and fertilizer application in farms that were obstacle rich. Other issues related to the lack of awareness and special education on precision agriculture in animal farming using UAV technology were also highlighted. Furthermore, [PERSON] et al. [22] presented a comprehensive review of the state-of-the-art techniques incorporated with UAVs for livestock. The authors highlighted various pressing issues, challenges and opportunities associated with livestock management. AI and ML have drawn growing research interests and are ubiquitously emerging in many fields due to their capability to model systems through learning from data [23]. Recently, studies and findings have unveiled the potential benefits of deploying AI and machine learning techniques and UAVs for effective livestock farming [2, 3, 8, 24, 25, 26]. Studies have shown the feasibility of using UAV video monitoring to predict the food eating behaviour of rangeland-raised Paramuri Criollo non-nursing beef cows [2]. To address the problem associated with animal counting, a computer vision pipeline that uses DNN architecture for automated Holstein Friesian cattle detection and identification was proposed in [24]. The authors introduced a video processing mechanism to efficiently monitor dynamic cattle footage filmed by UAVs. However, the UAV was manually flown and only captured data within small-sized and relatively spaced herds. [PERSON] et al. [25] presented the use of artificial intelligence techniques for real-time analysis and cattle monitoring using the information captured by drones. The authors used a camera installed in the drone to take images that were later analysed using Convolutional Neural Networks (CNNs) for cattle identification captured in the images. However, the model could not determine the number of animals in a cluster with utmost precision. Furthermore, a test bed implementation that used deep learning algorithms was designed for precision livestock detection and counting from aerial images captured by drones. In the same vein, the use of UAVs to track the postural position of cattle and sheep was studied in [8] to find the optimal number of UAVs that minimizes the UAV-animal distance using a streaming K-means clustering algorithm. All the targeted herds were fitted with global positioning system (GPS) neck-bands to monitor their movements.