## 2 Related Works
### Limitations of Related Works  
Despite advancements in integrating IoT platforms and machine learning techniques with smart agriculture, several limitations persist in the current body of research. A major challenge lies in the fact that many studies narrowly focus on specific aspects of data collection or sensor management, often overlooking the broader and integrated scope necessary for comprehensive data analysis and decision making in real-world agricultural scenarios.  
For example, while much attention has been given to improving sensor reliability and optimizing data acquisition through IoT technologies, there is still a lack of integrated methodologies that seamlessly apply machine learning techniques to automate decision-making processes at scale in smart farming environments. Most studies tend to focus on specific use cases, such as irrigation or soil moisture monitoring, but fail to provide solutions that consider the complexity and variety of agricultural data across different environments and applications.  
Another limitation is an over-reliance on traditional non-machine learning methods, such as simple thresholding, pattern detection, or statistical analysis (e.g., ARIMA models), for analyzing time-series data. While these techniques are suitable for basic data analysis, they fall short when applied to large-scale datasets or more complex agricultural systems in which sensor data may have temporal or spatial dependencies. Furthermore, these conventional approaches often depend heavily on manually labeled data, which limits the scaling of applications for real-world farming and the ability to adapt machine learning models for dynamic, evolving environments.  
Many existing studies also fail to realize the full potential of real-time decision-making systems, where latency and connectivity issues pose significant challenges, particularly in rural and agricultural settings. Although some progress has been made with cloud computing, issues related to latency, bandwidth, and dependence on mobile networks often impede real-time responsiveness. Recent trends toward fog and edge computing have opened new possibilities for real-time data processing closer to the source, yet the integration of these technologies into agricultural IoT systems is still in its early stages and requires further exploration.  
Another significant gap in the literature is the lack of attention given to automated labeling techniques such as pseudo-labeling, which can drastically improve the scalability and efficiency of time-series data analysis in smart agriculture. While pseudo-labeling has shown promise in certain applications, its potential for labeling agricultural sensor data and improving model accuracy remains underexplored. The few studies that have adopted pseudo-labeling often fail to combine this technique with advanced machine learning methods, which are necessary to manage the complexity and diversity of agricultural time-series data.  
Additionally, most current research on anomaly detection and time-series data analysis focuses on surface-level anomaly detection and classification but does not study more advanced techniques such as semantic data segmentation. This can limit the accuracy and precision of predictive models, especially for noisy or incomplete datasets. Another limitation is the tendency of existing models to analyze entire datasets without segmentation into smaller, more meaningful units based on short-term changes. This often leads to model inaccuracies, as the complexity and inherent noise in agricultural data can distort the results.