# Lamb Behaviors Analysis Using a Predictive CNN Model and a Single Camera
## 4 Discussion
### Limitations  
This research had limitations. First, future research should compare the performances of different current deep learning systems, not only those commonly mentioned but Faster R-CNN and YOLO\({}_{V3}\) as well. However, by making the dataset used in this work public, other researchers can use it for training and validation, facilitating experimentation, and replicating the experiments to improve performance. Second, we have not found works related to detecting lamb activities or detecting locomotion problems in lambs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Objects** & **Dataset** & **Precision** & **Categories** & **Tracking** & **Analisis** \\ \hline YOLO\({}_{V4}\) & Red foxes [11] & 8913 & 99.91\% & Sitting, Lying, & No & Individual \\  & & & & & Standing, Lying, & Yes & Group \\ YOLO\({}_{V4}\) & Lambs (Proposed) & 9652 & 99.85\% & Standing, Lying, & Yes & Group \\  & & & & & Eating & & \\ YOLO\({}_{V4}\) & Goats [5] & 1200 & 97.49\% & Drinking, Eating, & Yes & Group \\ Faster R-CNN & Boilers [29] & 9040 & 93\% & Feeder, Eating bird, & Yes & Group \\  & & & & & Bird around feeder & & \\ Faster R-CNN & Pigs [29] & 7277 & 80.20\% & Pig, Pig lying & No & Group \\ YOLO\({}_{V3}\) & Dairy cows [27] & 11,754 & 66.00\% & Xleft, Yleft, Wright, & No & Group \\  & & & & & Oleft, Cright, Left, Hight & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with related work.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Total** & **True** & **False** & **True** & **False** & **General** \\  & **Objects** & **Positives** & **Positives** & **Negatives** & **Negatives** & **Precision** \\ \hline Video\_1 & 4 & 4 & 0 & 0 & 0 & 100 \\ Video\_2 & 4 & 3 & 0 & 1 & 0 & 100 \\ Video\_3 & 2 & 1 & 0 & 1 & 0 & 100 \\ Video\_4 & 2 & 1 & 0 & 1 & 0 & 100 \\ Video\_5 & 1 & 0 & 0 & 1 & 0 & 100 \\ Video\_6 & 3 & 2 & 0 & 1 & 0 & 100 \\ Video\_7 & 4 & 4 & 0 & 0 & 0 & 100 \\ Video\_8 & 1 & 0 & 0 & 1 & 0 & 100 \\ Video\_9 & 3 & 2 & 0 & 1 & 0 & 100 \\ Video\_10 & 3 & 2 & 0 & 1 & 0 & 100 \\ Video\_11 & 3 & 2 & 0 & 1 & 0 & 100 \\ Video\_12 & 3 & 2 & 0 & 1 & 0 & 100 \\ Video\_13 & 3 & 1 & 0 & 1 & 1 & 66.66 \\ Video\_14 & 3 & 0 & 0 & 1 & 2 & 33.33 \\ Video\_15 & 5 & 3 & 0 & 1 & 1 & 80 \\ Video\_16 & 5 & 0 & 0 & 0 & 5 & 0 \\ Video\_17 & 5 & 2 & 0 & 0 & 3 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Predictive model results.  
The developed model detected and tracked multiple objects; other research did not address this. This work focused on creating an intelligent system that detected and registered activities--based on this, it also estimated health, obtaining good results in the activity detection task with a mAP of 98%. Third, the selection of videos of beginning feeding was motivated by the fact that lambs showed different behavior. The results displayed were not considered as a 24 h estimate. Fourth, in a generic pen of regional producers implementation the detection of lamb activities. In these conditions, we record videos in 14 days. Therefore, it is a reliable estimate concerning other preview works, which is adaptable. Additionally, the analysis showed that it was possible to apply these deep learning techniques to studying behavior and detecting diseases.