## 5 Conclusions  
This study explored the use of machine learning algorithms to analyze time-series data in the context of smart agriculture, where monitoring and managing environmental conditions help to improve sustainable farming practices. The proposed methods for data preprocessing, waveform segmentation, and state classification were evaluated in a smart farming environment and achieved high accuracy in sensor state identification. The model demonstrated an overall accuracy rate of 89%, with automated labeling resulting in a 30% reduction in data processing time. However, the model showed room for improvement in the classification of normal states, suggesting that additional refinement is necessary.  
Future research may focus on enhancing model generalization by collecting data from various agricultural environments and exploring deeper neural network architectures. Real-time data processing capabilities will be essential in supporting the deployment of these  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1 Score** \\ \hline Irrigation & 92.5\% & 72.5\% & 81.2\% \\ \hline Abnormal & 100\% & 100\% & 98.7\% \\ \hline Normal & 71\% & 97.4\% & 83.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation metrics per class.  
Figure 10: Ground truth and prediction for semantic segmentation.  
models for smart farming. Additionally, integrating data from multiple devices, such as soil moisture and temperature sensors, can contribute to developing more comprehensive monitoring systems. Improving the interpretability of machine learning models will also be valuable for gaining insights into the models' decision-making processes.  
Our study introduced a pseudo-labeling approach to address the challenges of manual labeling for large-scale time-series data, leading to improvements in accuracy, scalability, and efficiency. This approach holds significant potential not only for smart agriculture but also for other domains requiring precise, rapid data analysis. By automating the labeling process, the model enhances its capability to manage large datasets efficiently, thereby supporting timely and informed decision making in agricultural practices.  
In future work, we plan to expand the scope of our research to include IoT advancements and data security frameworks relevant to smart agriculture. This expansion will provide a more holistic understanding of the technological challenges in smart farming and further enhance the applicability of machine learning-based approaches. Ultimately, exploring the real-world impact of machine learning-driven decision making in agriculture will provide valuable insights for refining and extending these technologies, contributing to smarter and more sustainable farming practices.  
Conceptualization, M.J.; methodology, M.J. and D.-Y.K.; software, M.J.; validation, [PERSON] and [PERSON]-Y.K.; formal analysis, [PERSON] and [PERSON]-Y.K.; investigation, [PERSON] and [PERSON]-Y.K.; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]-Y.K.; visualization, [PERSON]; supervision, [PERSON]-Y.K.; project administration, D.-Y.K.; funding acquisition, D.-Y.K. and M.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) grant, funded by the Korean Government (MSIT) (No. 2021R1C1C1013133); the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF2018R1A6A-IA03025109); and the Soonchunhyang University Research Fund.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.