## 2 Related Work
In a study by [PERSON] et al., a 3D Kinect camera was used to capture images of cows from lateral and dorsal perspectives [14]. They used several measurements obtained from these images to run a Lasso regression model to estimate body weight. [PERSON] et al. used a 3D Kinect camera as well to take images of dairy heifers to estimate height and body mass [15]. Their approach was to fit an ellipse to the body image to calculate some features. Then, they used these features to train various linear regression models. [PERSON] et al. created a system to estimate the body weight of cows using a 3D camera system [16]. Similar to previous studies, they extracted morphological features from 3D images such as hip height, hip width, and rump length. Combining these features with some other cow data such as days in milk, age, and parity, they trained multiple linear regression models. Another study that employed a 3D Kinect camera is the one conducted by [PERSON] et al. [17]. They captured body images of pigs using two cameras from top and side, and then extracted body dimensions from images such as heart girth, length, and height using image processing techniques. They developed linear and non-linear regression models based on these dimensions to predict weight. Advanced scanning devices were also introduced in body weight estimation studies. [PERSON] et al. used a 3D full-body scanning device to obtain very detailed body images of cows [18]. Then, they computed body measures from these 3D images such as volume, area, and other morphological traits. Using these measures, they trained and compared several regression models. [PERSON] et al. developed a system to make use of thermal camera images of cows to extract body features and then used them in several linear regression models to estimate body weight [19]. Stereo vision techniques are also used in the determination of live animal weight. [PERSON] et al. developed a regression model to analyze and estimate the body size and live weight of farm pigs under indoor conditions in a farm [20]. Their system was based on a binocular stereo vision system and a special fence system through which animals passed for taking the measurements. They segmented the images obtained from the stereo system using a depth threshold and predicted the body length and witness height, then the body weight. Some other notable studies using stereo vision are by [PERSON] et al. and [PERSON] et al. [21; 22]. Deep learning-based approaches are very popular today due to their success in image-processing applications. Deep learning is a special form of neural network algorithm. Although it has achieved the most advanced results in many fields, its use in determiningthe weight of livestock is limited [23]. There are studies that apply deep learning algorithms and determine the weight of pigs [24; 25]. When we examine the prior research on the estimation of live body weight of farm animals such as pigs, cattle, cows, and heifers, there is a common approach to capturing images of animals that the animals are forced to move into special types of boxes or fences, or they are forced to pass through a special passage. This operation is very similar to traditional weight measurement with scales, and therefore, it also requires the separation of animals from their natural environment, and it causes stress-related problems in their health and milk yield [3]. Our proposed approach is superior to this in that animals' pictures are taken in their natural environments without the need for a special measurement station. Additionally, our approach is totally contactless and pictures do not need to be taken from very close proximity, unlike previous studies. One other advantage of our proposed approach provides a simpler structure and setup composed of relatively cheaper hardware that can be accessible and affordable for many farms of small to large scale. Last but not least, we employ modern and state-of-the-art deep learning-based image processing techniques in our system, which is one of the few such studies.