## 3 Results and Discussion
### GridMask Data Augmentation Results  
The results in Section 3.2 indicate that the ImageNet policy enhances performance more than the Cifar-10 policy. Therefore, GridMask was applied to the images augmented using the ImageNet policy with a 50% probability. The detection performances for training the model on data augmented using AutoAugment and GridMask are presented in Table 5.  
Compared with the original dataset, the _precision_ decreased by 0.5%, _recall_ increased by 9.2%, and _mAP_ improved by 4.8%, indicating that GridMask enhanced the generalization performance of the detection model. Additionally, the model could recognize object features using only partial information regarding the object. Consequently, its object detection capability in various environments was improved. The integration of GridMask and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Augmentation** & **Behavior** & _Precision (\%)_ & _Recall (\%)_ & _mAP (\%)_ \\ \hline \multirow{4}{*}{Cifar-10} & Resting & 93.3 & 91.7 & 95.0 \\  & Communion & 87.6 & 81.6 & 87.0 \\  & Feeding & 79.4 & 71.4 & 74.5 \\  & Drinking & 95.0 & 99.4 & 98.9 \\  & Eating & 87.5 & 96.1 & 97.0 \\  & All & 88.6 & 88.0 & 90.5 \\ \hline \multirow{4}{*}{ImageNet} & Resting & 94.0 & 88.4 & 93.5 \\  & Communion & 83.7 & 79.9 & 87.2 \\ \cline{1-1}  & Feeding & 83.7 & 69.0 & 78.9 \\ \cline{1-1}  & Drinking & 96.8 & 96.6 & 98.8 \\ \cline{1-1}  & Eating & 88.2 & 96.4 & 97.0 \\ \cline{1-1}  & All & 89.3 & 86.1 & 91.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of the cattle behavior detection model for the dataset augmented using AutoAugment with the Cifar-10 and ImageNet policies. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Behavior** & _Precision (\%)_ & _Recall (\%)_ & _mAP (\%)_ \\ \hline Resting & 92.3 & 86.6 & 92.6 \\ Communion & 84.3 & 68.7 & 81.4 \\ Feeding & 82.2 & 67.8 & 73.7 \\ Drinking & 92.5 & 89.0 & 96.4 \\ Eating & 90.2 & 95.9 & 96.4 \\ All & 88.3 & 81.6 & 88.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of cattle behavior detection for the original dataset. AutoAugment enhanced the training dataset diversity, resulting in notable improvements in the model performance. However, owing to a 6.4% decrease in the _precision_ score for the eating class, the _precision_ for all classes dropped by 0.5%. The eating class labeled behaviors such as cows putting their heads through the feeding fence or placing a calf's head inside the feed container. Therefore, the labels exhibited two distinct positions and shapes. By applying AutoAugment and GridMask, parts of the feed container and fence were obscured in the image transformations, preventing adequate model training on these behaviors. Therefore, cattle that did not exhibit any specific behavior were incorrectly detected as eating, which decreased the _precision_ for the eating class. Recall and _mAP_ are important metrics for livestock farming. [PERSON] et al. [27] noted that high _recall_ and _mAP_ scores help effectively detect cattle. Additionally, a high _recall_ score for livestock behavior detection minimizes the non-detection rate of behaviors related to diseases or extras. Thus, it is directly related to livestock health and ultimately affects the productivity of agricultural farms and is a crucial evaluation metric. The proposed detection model showed a 9.2% improvement in _recall_ when it was trained on data augmented using AutoAugment and GridMask compared to the original dataset. This improvement is expected to provide valuable information, potentially leading to a substantial increase in labor efficiency and reduced labor costs for farmers. Figure 8 presents the detection results of the model with both AutoAugment and GridMask applied. Upon reviewing the detection outcomes of the model integrating these two augmentation techniques, it becomes evident that the synergy between AutoAugment and GridMask significantly enhances the model's capability to detect and classify cattle behaviors accurately.