# Livestock Management on Grazing Field: A FANET Based Approach
## 2 Literature Review  
Livestock management in widespread production systems may be thought-provoking, particularly in huge regions. The use of UAVs to gather images from the region of concern is rapidly becoming a feasible alternative. Nonetheless, proper processes for extracting pertinent data from the images are still rare. Conventionally, the recognition of livestock through a UAV is centered on a simple process involving a video recording of the pasture where the livestock is spotted and calculated manually by a human viewer. This practice was found to be valuable in the detection and counting of cattle in a quarantine environ  
Figure 1: Conceptual Framework. ment [5]. However, this method is manual and always involves the presence of a human observer. The authors in the work of [3; 6; 7] present the parameters and key limitations, current regulations, potential requirements, and challenges for operating UAVs in smart agriculture. To computerize the livestock detection and counting method, the number of livestock was added up in the video frame by detaching its image from the background and applying thresholding on each image frame of the video sequence [8; 9; 10]. The ability of Unmanned Aircraft Systems (UAS) overflights for cattle surveillance was assessed in [11]. The information attained from the Unmanned Aircraft System (UAS) image was used to model the cattle distribution, and the outcomes were related to bio-logged cattle. The prospect of using UAV video surveillance to predict the food intake of non-nursing beef cows was examined where the cow feeding pattern was determined from the processed video files. The results suggest that UAV surveillance could be vital in monitoring cow feeding behavior [12]. The work of [13] suggested a general smart video surveillance system and studied some glitches in cow performance analysis using an intelligent image-based monitoring system framework with a hidden Markov process. while the authors in the work of [14] suggested a new robotic animal herding system centered on a system of independent barking drones. Such a system aims to substitute old herding approaches so that an enormous amount of farm animals can be speedily collected from a sparse status and then driven to a selected place. [PERSON] et al. propose a method to process images taken by a commercial drone to automate the tracking of animal activities using a combination of thresholding and supervised classification methods [15]. [PERSON] et al. use a Proportional Integral Derivative (PID) controller on four quadrotor UAVs to guide four animals into their pen within the minimum time by creating noises of predators modeled with an exponential function to provide a solution to the cattle roundup problem [10]. The geographical proximity used to examine grazing behavior and social structure is a critical indicator of performance in cow behavior. As such, [PERSON] et al. developed a competent way to compute the spatial proximity of beef cattle, employing UAV-based image acquirement and photogrammetric analysis. Still-frames pulled out from the UAV video screenshots were used to produce Orth mosaics, revealing that groups of correlated sets were nearer than non-correlated ones [16]. [PERSON] et al. offered a real-world method that used UAV's and verified its use at a distinctive household pasture to examine the hourly spatial distribution of each yak [17]. [PERSON] et al. explored the use of UAVs to detect and round up sheep by developing a prototype controlled from a laptop base station running on LABVIEW [18]. [PERSON] et al. discovered the relationship between object detector performance and spatial degradation for livestock. Factual data was established using focus drone images and then down sampled to various ground sample distances (SSDs). Apart from the simple method of livestock detection, advancements in Artificial Intelligence (AI) and Machine Learning (ML) have allowed researchers to detect livestock using a pre-trained Convolutional Neural Network (CNN)-based architecture.