## 2 Related Work  
In this section, we provide essential background on livestock weight estimation with a review of significant past research. Our focus in this review is on the work with indirect measurement approaches based on image processing techniques. We also summarize them in Table 1.  
There are several studies in the literature that are based on image processing techniques on 2D images. In a study by [PERSON] et al., the live body weight of cattle was estimated using dorsal area images taken from above using a kind of fence system [7]. Their system first performs segmentation and then generates a convex hull around the segmented area to obtain features to feed a Random Forest-based regression model. [PERSON] and [PERSON] performed a study where they predicted the live weight of co \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Animal Type** & **Image Type** & **Method** & **Environment** \\ \hline
[7] & cattle & 2D & segmentation + convex hull, random forest & fence system \\
[8] & cow & 2D & ANN Regression & - \\
[9] & pig & 2D & ANN Regression & - \\
[10] & cattle & 2D & gabor filter, fuzzy logic & - \\
[11] & cow & 3D & segmentation & fence system \\
[12] & Pig & 3D & segmentation, linear regression & - \\
[13] & cattle & 3D & segmentation, linear and non-linear regression & - \\
[14] & cow & 3D & Lasso regression & fence system \\
[15] & heifer & 3D & ellipse fitting, linear regression & narrow passage \\
[16] & cow & 3D & linear regression & - \\
[17] & pig & 3D & linear and non-linear regression & - \\
[18] & cow & 3D full-body scan & linear regression & special scanning station \\
[19] & cow & thermal & linear regression & - \\
[20] & pig & stereo vision & least squares regression & fence system \\
[21] & calf & stereo vision & linear regression & - \\
[22] & calf & stereo vision & linear regression & - \\
[23] & heifer & 2D & deep learning-based image processing and & - \\  & & regression & regression & \\
[24] & pig & 3D & deep learning-based image processing and & - \\  & & regression & - \\
[25] & pig & 2D & deep learning-based image processing and & - \\  & & regression & - \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of the previous studies. regression model [8]. They determined various body dimensions such as wither height, hip height, body length, and hip width applying photogrammetric techniques on images of cows captured from various angles. [PERSON] et al. developed an image processing-based system to estimate the body weight of pigs [9]. Their main approach was to process images captured from above to extract features such as area, convex area, perimeter, and so on. Then, using these features, they trained an ANN-based regression model for weight prediction. A Fuzzy Rule-Based System was also utilized in cattle weight estimation by [PERSON] and [PERSON] [10]. They obtained 2D side images of cattle from a very close distance of 1.5 m. After applying the Gabor filter to the images, they obtained body length and circumference as features. Finally, they designed a fuzzy logic system to estimate body weight. Three-dimensional imaging techniques also found application in body weight estimation systems. [PERSON] et al. used a 3D Kinect-like depth camera to obtain the views of cows from above as they passed along a fence [11]. Applying thresholding, they obtained the segmented area of cows to reach a body weight estimate. In another study where a 3D Kinect camera was used, [PERSON] et al. processed images taken from above of pigs by applying two segmentation steps [12]. Then, they extracted features from segmented images such as body area, volume, width, and height to feed a linear regression model to obtain the weight estimate. In a similar study, [PERSON] et al. developed a system to capture images of cattle using a 3D Kinect camera [13]. They trained and compared a number of linear and non-linear regression models by feeding them with features extracted from segmented images.