Developing a semantic framework for categorizing IoT agriculture sensor data: A machine learning and web semantics approach

Abstract

This study introduces a Semantic Framework for Categorizing IoT Agriculture Sensor Data, leveraging Machine Learning and Web Semantics. IoT sensors in agriculture generate vast real-time data on crucial factors like soil conditions and weather, promising optimization in resource use and crop yields. While machine learning aids data categorization, semantic aspects often remain unexplored. By combining machine learning with web semantics (RDF and OWL), this research establishes a structured framework that not only categorizes data but also links it to actionable farming recommendations. Methodologically, it involves data collection, preprocessing, machine learning, and semantic integration. Performance evaluation through metrics and visualizations reveals the framework's effectiveness, aiding decision-making in precision agriculture. This study contributes to IoT-based precision agriculture by bridging the gap between raw sensor data and actionable insights, empowering a semantic framework for contextual categorization and recommendation generation. The fusion of machine learning and web semantics holds transformative potential for agriculture, enhancing data management and decision-making processes.