# Livestock Management on Grazing Field: A FANET Based Approach
## 2 Literature Review
For instance, an adjusted version of R-CNN was employed to identify and count livestock on a grazing field [19]. In this method, a selective search algorithm was used to generate a region proposal and then applied a CNN to extract the features in the region, which were then later classified using a Support Vector Machine (SVM). The confidence value was obtained by applying it to a linear bounding box. [PERSON] et al. scrutinized twelve sheep's habits and normal responses to a drone to fit mathematical models of shepherding to the new dimension. The model targets make it realistic for AI to advance the independence of farmers in shepherding above the ground [20]. [PERSON] et al. proposed a cattle counting scheme incorporating a deep learning model for the rough animal position and color space manipulation to increase the contrast between animals and the background [21]. [PERSON] et al. investigated the prospect of utilizing an incline angle to increase the expanse of the acquired image in cattle monitoring. This feature was realized by creating a model for animal detection using Deep Convolutional Neural Networks. Fact findings show that oblique images can be magnificently utilized in some circumstances, but certain real restrictions must be solved for the method to be attractive [22]. [PERSON] et al. recommend a deep neural network method for livestock recognition employing UAVs with onboard deep learning inference [23]. [PERSON] et al. suggested a technique for identifying and counting cattle, in above-the-ground images acquired by UAVs, based on CNNs and a graph-based optimization technique to remove detected duplicated images [24]. [PERSON] et al. propose a cattle detection and counting system based on CNNs using aerial images taken by a UAV [25]. Owning that the future routing protocols depend on the nature of the communication link, the works in [26; 27] present the design of an interface protocol for an indoor Flying Ad-hoc Network-specific routing protocol, using light fidelity as a communication link. The focus was to achieve high throughput. However, practical routing problems encountered during UAV operations are not considered. To solve practical routing problems encountered during UAV operations, ref. [28] proposed an optimized solution for the problem of minimum time coverage of ground areas using multiple UAVs with image sensors. This is achieved by determining the geographic coordinates a single UAV would cover in a minimum time and then formulating mixed-integer linear programming to route the UAVs over the geographic area. The UAVs required to cover a particular area could then be selected. However, the UAV routing strategy does not consider possible collisions among the UAVs. Hence, to avoid link breakage during information transfer, the communication path between multiple UAVs is optimized to improve communication links in flying Ad-hoc networks using smell agent optimization and the Particle Swarm Optimization (PSO) algorithm [29]. The reviewed studies show that several works have been conducted in livestock management. However, only a few works considered the geographic coordinates of the grazing field for practical implementation before optimizing the UAV's performance in the field in terms of communication and flight patterns. Hence, this paper presents a solution based on the practical geographic coordinates of the grazing field for livestock perimeter tracking and management.