## 4 Results  
In this section, we present the prediction performances of the neural networks trained in a comparative manner. The performance levels of the networks are shown in Figure 11.  
The success rate of the ANN-1 network is higher than the ANN-2 network. The reason for this is the inability of the images taken from the back to reveal the general body dimensions of the animal. On the other hand, the performance rate of the ANN-3 network is higher than the other two networks. This is because the network was trained with data from images taken from both angles of animals. Randomly, 10% of the taken images were not used in the training but in the testing of the estimated animal weights. Weight estimation was made separately for the three proposed networks and the results are shown in Tables 5-7. Figure 11: Loss graphs of artificial neural networks: (**a**) Loss graph of ANN-1 network; (**b**) Loss graph of ANN-2 network; (**c**) Loss graph of ANN-3 network. Figure 10: Basic Operation Steps for Weight Prediction. As seen in Table 5, the estimations for the test data made by the ANN-1 network vary between approximately \(\pm\)50 kg. Note that ANN-1 is only trained with data obtained from the side. In Table 6, the error amounts in the estimations made by the ANN-2 network, which was trained only with photographs taken from the back, vary between approximately \(\pm\)50 kg. However, the error rates increased dramatically in animals with id numbers 36, 70, and 81. This significantly reduces the accuracy of the network trained with images taken from behind. The reason for this is the inability of the images taken from the back to reveal the general body dimensions of the animal. Table 7 shows the results obtained from the ANN-3 network trained with the entire dataset. In most cases, the predictions were made with a margin of error of approximately \(\pm\)20 kg, and much more successful results were obtained than the first two networks. The animal image taken in prediction number 36 with a high amount of error is very close to the camera plane. The image of the animal taken very close to the camera plane causes serious errors as it cannot be adequately represented in the dataset. For this reason, it would be more appropriate to take the images to be obtained at reasonable distances not very close to the camera plane. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ID** & \begin{tabular}{c} **Left Side** \\ **Shooting (Pixel)** \\ \end{tabular} & \begin{tabular}{c} **Right Side** \\ **Shooting (Pixel)** \\ \end{tabular} & \
\begin{tabular}{c} **Side Pixel** \\ **Difference (Pixel)** \\ \end{tabular} & **Real Weight (kg)** &
\begin{tabular}{c} **Estimated** \\ **Weight (kg)** \\ \end{tabular} & **Difference (kg)** \\ \hline
9 & 9819 & 10,676 & 24 & 291 & 302.66 & \(-\)11.66 \\
21 & 8818 & 9227 & 11 & 376 & 415.95 & \(-\)39.95 \\
36 & 45,864 & 45,521 & 59 & 423 & 632.53 & \(-\)209.53 \\
44 & 10,000 & 10,126 & 11 & 418 & 422.55 & \(-\)4.55 \\
47 & 28,421 & 29,072 & 25 & 447 & 492.18 & \(-\)45.18 \\
64 & 5726 & 5492 & 6 & 458 & 441.39 & 16.60 \\
67 & 6002 & 6056 & 8 & 409 & 425.11 & \(-\)16.11 \\
70 & 6753 & 6239 & 18 & 131 & 332.30 & \(-\)201.30 \\
81 & 3831 & 3852 & 10 & 294 & 391.84 & \(-\)97.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Prediction values on the test dataset of ANN-2 network.