## 2 Related Works  
### Time-Series Data Usage in Smart Agriculture  
Time-series data, characterized by their temporal sequence, are integral to smart agriculture, as they enable real-time monitoring and analysis of critical farming variables. The sensors and IoT devices used in agricultural settings generate continuous streams of time-stamped data, reflecting various phenomena, such as soil moisture levels, crop health indicators, and environmental conditions. By leveraging the analytical power of time-series data, agricultural stakeholders can gain detailed insights into crop yields to forecast environmental trends and optimize resource allocation. For example, models that predict irrigation needs based on soil moisture data can help conserve water and improve crop yields. Similarly, temperature and humidity monitoring can enable precise climate control in greenhouses, fostering better crop growth. Accurate and efficient analysis of time-series data is essential for supporting data-driven decisions that contribute to sustainable agricultural practices. Agriculture plays an important role in global economies and sustainability. As the world's population continues to grow, the necessity of increasing food production has led to a focus on enhancing, automating, and optimizing agricultural activities for crops and livestock. These efforts, however, often have negative environmental impacts. Smart farming technologies aim to address these challenges by optimizing productivity while reducing costs, waste, and environmental footprint. Moreover, these technologies contribute to improving the quality of crops and livestock. A wide variety of sensors is used in precision agriculture to capture several types of measurements. In Sensing Approaches for Precision Agriculture by [PERSON] and [PERSON], various sensors are presented, including those currently available on the market, as well as some under research or development. These sensors perform tasks such as soil sensing, crop health assessment, and disease detection. Other sensors discussed in this work have been deployed on unmanned aerial vehicles (UAVs) [7]. Several research proposals have emerged that leverage sensor data to optimize resource utilization. For example, a study by [PERSON] et al. presented a system that optimizes water consumption in crop irrigation by using decision-making processes based on a machine learning algorithm (KNN), supported by an ontology and data from humidity, temperature, and light sensors [8]. Technological and scientific advancements in precision crop and livestock farming have also been explored. [PERSON] et al. highlighted applications that use devices to monitor livestock using GPS-based geopositioning and machine learning solutions designed to detect animal discomfort, thereby reducing mortality and improving welfare. Additionally, some systems use UAVs or robots for crop fertilization and agricultural process automation using sensors and AI-driven actuators [9]. For livestock monitoring, one innovative approach involves the use of multi-sensor collars to monitor individual animals. These collars collect animal activity data and are integrated into cloud-based systems to provide real-time monitoring and proactive management. [PERSON] et al. described how this solution can improve the extraction and analysis of livestock monitoring data [10]. The use of sensors in crop harvesting supports activities such as plant sorting, counting, and health monitoring. [PERSON] et al. reviewed studies in this area, including the automation of harvesting tasks with UAVs and robots. These advancements highlight the growing reliance on sensor-based automation in precision farming [11]. Pesticide spraying is essential for protecting crops from diseases and pests, although manual spraying can expose humans to significant health risks. [PERSON] and [PERSON] examined how UAVs can automate pesticide-spraying tasks to improve spraying precision and ensure human safety [12].