Autonomous Integration of Internet of Things and Causal Machine Learning-Based Business Intelligence for Precision Agriculture Financial Risk Mitigation
Keywords:
Autonomous Bussiness Intelligence, IoT, Bi-LSTM, Causal Machine Learning, Agricultural ProfitabilityAbstract
Climate uncertainty and environmental data dynamics pose major challenges to the stability of agricultural sector profitability in the Economy 5.0 era. This research aims to develop an autonomous Business Intelligence (BI) system that synchronizes Internet of Things (IoT) rain sensor data with real-time financial profit projections. Using a systems development approach, this framework leverages conceptual data models to integrate heterogeneous data streams into a structured Big Data architecture. The system's analytical core combines a Bi-Directional Long Short-Term Memory (Bi-LSTM) algorithm for precise rainfall prediction with Causal Machine Learning to analyze cause-and-effect relationships across economic metrics. The results show that this integration enables transparent prescriptive decision-making in pre-rain risk mitigation strategies, such as optimizing fertilization and harvest schedules. The system's strength lies in its autonomous ability to detect data drift and independently retrain models to maintain accuracy amidst changing extreme weather patterns. This implementation significantly contributes to operational efficiency and increased profit margins by transforming IoT data into robust and adaptive business intelligence.
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