###### Abstract  
This study proposes an automated data-labeling model that combines a pseudo-labeling algorithm with waveform segmentation based on Long Short-Term Memory (LSTM) to effectively label time-series data in smart agriculture. This model aims to address the inefficiency of manual labeling for large-scale data generated by agricultural systems, enhancing the performance and scalability of predictive models. Our proposed method leverages key features of time-series data to automatically generate labels for new data, thereby improving model accuracy and streamlining data processing. By automating the labeling process, we reduce dependence on manual labeling, which is often labor-intensive and prone to errors in large datasets. This approach enables the efficient preparation of labeled data for applications such as anomaly detection, pattern recognition, and predictive modeling in smart agriculture. Experimental results demonstrate that the automated labeling model achieves 89% accuracy in agricultural environments and reduces data processing time by 30%. Future research will focus on extending the model's applicability to diverse agricultural settings, enhancing generalization performance, and improving real-time processing capabilities, thereby advancing intelligent and sustainable smart agriculture systems. s +
Footnote †: journal: Applied sciences  
a pseudo-labeling approach to automate time-series data labeling, significantly enhancing the convenience, efficiency, and accuracy of data preparation for machine learning models [4]. Pseudo-labeling is a semi-supervised learning method that uses a small amount of labeled data to assign labels to a larger, unlabeled dataset, expanding the labeled data through iterative refinement. This approach is highly useful in smart agriculture for rapid data analysis and real-time decision making, as it improves scalability, accuracy, and consistency in data preparation while reducing manual effort [5; 6]. Figure 1 provides an overview of the intent-based IoT platform proposed in this study. This platform integrates machine learning and artificial intelligence to autonomously manage smart agricultural environments. By analyzing real-time environmental data and system feedback, the platform anticipates and adjusts key agricultural operations, such as irrigation, fertilization, and climate control. This platform enhances farming efficiency by enabling data-driven decision making with minimal manual intervention. Our proposed machine learning algorithm performs this task by first extracting feature regions through waveform segmentation, which captures the key characteristics of the time-series data. These features are then used by the machine learning model to classify and predict the operational status of agricultural sensors and devices. The process is then further automated by generating labels for new data based on learned features, facilitating the development of robust and scalable machine learning models. This automated approach enables system developers to manage vast quantities of agricultural time-series data more efficiently, leading to more accurate and responsive agricultural management systems. By integrating pseudo-labeling with machine learning, the proposed method provides a significant improvement over traditional manual labeling techniques. It supports the development of intelligent agricultural systems that can enhance operational efficiency and contribute to sustainable and scalable farming practices. In this study, we propose a novel approach to automate the labeling of time-series data in smart agriculture, aiming to enhance the scalability and accuracy of machine learning applications in this field. The remainder of this paper is organized as follows. Section 2 reviews related works, providing a comprehensive analysis of existing methodologies and identifying current limitations in time-series data labeling and automation within the context of smart agriculture.