## 4 Conclusions  
The livestock sector faces challenges due to a decreased labor force, increasing workloads, and heightened stress for farmers [28]. In response, this study proposes a cattle behavior detection model utilizing 2D RGB cameras to automate and simplify monitoring processes within livestock farms. In this study, we enhanced model performance by 9.2% for _recall_ and 4.8% for _mAP_ using AutoAugment and GridMask compared to the original datasets. This automation can reduce the workload on farmers by providing real-time insights into cattle behavior and contributing to the early detection of health issues. It has the potential to significantly cut veterinary costs and reduce losses from livestock health problems. When estrus detection in cattle is considered, they exhibit increased activity, decreased resting, and reduced feed intake, which facilitates estrus detection through behavioral analysis [29]. Employing PLF technology can yield up to EUR 2729 in annual economic benefits per farm over non-PLF methods [30]. Thus, this research could contribute to sustainable livestock farming and profitability through reduced labor costs and increased livestock production. This study has a limitation in that the _precision_ slightly decreased in the model trained on data augmented using AutoAugment and GridMask compared to that trained on the original data. Therefore, future studies should use images enhanced with AutoAugment and GridMask after filtering. Additionally, labels for objects whose features have disappeared or are heavily modified because of augmentation policies can be problematic. Removing these labels using a model trained only on the original data might resolve the performance decline. In addition, this study used data collected from a single farm with the same recording equipment, which could have resulted in model bias toward this specific farm, and its performance might not be adequately validated using data from other farms. To address these issues, we are installing different cameras, not limited to those used in this study, in various barns to collect data. In reviewing various studies on cattle behavior detection, [PERSON] et al. [31] employed the YOLOv3 model to detect 15 classes of cow behavior in 1920 \(\times\) 1080 resolution images, achieving a _mAP_ of 78.8%. Similarly, [PERSON] and [PERSON] [32] achieved a _mAP_ of 91.5% utilizing the YOLOv5-L model for four classes at a resolution of 3840 \(\times\) 2160. Our study has shown similar or higher performance despite utilizing lower resolutions. Thus, future experiments incorporating diverse angles, barn environments, and resolutions could mitigate model bias and enhance the model's generalization performance. In this study, our methodology utilized the sum of gray levels from the entire image. Recognizing the potential for further refinement, we consider exploring a more targeted approach for future research. Specifically, we aim to investigate the application of summing  
Figure 8: The detection results of proposed model. gray levels within designated regions of interest. This is particularly beneficial in scenarios where vital information is compromised by background noise or other distractive elements. Focusing on regions could alleviate such challenges, facilitating a more accurate feature extraction of the target object. This strategy holds promise in domains where the significance of certain image areas is paramount. For example, in precision livestock farming (PLF), zeroing in on segments of aerial images that capture active livestock could enhance model detection accuracy. As we progress, our goal is to refine this approach for image analysis and streamline its application, making it versatile enough for broader use across various fields. This evolution will involve developing and meticulously tuning the method to automate the process for enhanced efficiency and applicability. Conceptualization, H.-c.C. and J.S.K. ; methodology, H.-s.S., T.-k.K. and H.-c.C. ; software, H.-s.-S. and T.-k.K. ; validation, H.-s.S. and T.-k.K.