IrrigaFlow: An IoT Architecture Integrated with Fuzzy Logic for Sustainable Agricultural Irrigation Optimization

Context: The growing demand for food security and the scarcity of natural resources pose global challenges, particularly in agriculture, which accounts for a significant portion of water consumption. In Brazil, agriculture holds substantial economic importance, but its intensive water use calls for more sustainable practices. Problem: Managing irrigation adaptively and efficiently is challenging due to the complexity of multiple environmental variables. This reduces water-use efficiency and negatively impacts agricultural productivity. Solution: IrrigaFlow is a modular architecture that automates irrigation using IoT, fuzzy logic, and distributed processing. It consists of three layers, namely IoT Module, Network Edge, and Cloud, enabling real-time monitoring and adjustments based on local environmental data. This approach optimizes water usage and improves responsiveness to climatic conditions. Information Systems Theory: Grounded in the Sociotechnical Systems Theory, this proposal balances advanced technology with human and organizational contexts, promoting efficiency and sustainability by dynamically adapting to environmental and operational conditions. Methodology: A qualitative interpretative approach was employed, combining case studies and simulations. Environmental data collection, fuzzy logic processing, and MQTT-based communication were validated to ensure the system’s effectiveness before practical implementation. Results: The system demonstrated efficiency in irrigation management by adjusting water volume and timing based on environmental variables, showcasing its potential to optimize water use in agriculture. Contributions and Impact on Information Systems: From an academic perspective, this work lays a foundation for research on distributed technologies applied to agriculture. For the industry, it offers a replicable model that enhances water efficiency and sustainability