<![CDATA[Purpose – This study addresses the challenge of maintaining optimal nutrient and environmental conditions in indoor hydroponic cultivation, where temperature, humidity, light intensity, and nutrient concentration interact dynamically. Many existing systems still depend on manual observation or fragmented monitoring, which limits real-time responsiveness and data-driven decision-making. Therefore, this study aims to design and implement a real-time Internet of Things (IoT)-based hydroponic monitoring system using the Deep Flow Technique (DFT) with an integrated architecture for continuous monitoring and analytical interpretation. Methods - An end-to-end IoT monitoring system was developed by integrating sensor hardware, wireless communication, backend processing, and a web-based analytics dashboard. The TDS, DHT21, and BH1750 sensors were connected to an ESP8266 microcontroller for real-time data acquisition and Wi-Fi transmission at 60-second intervals. The backend used NestJS with PostgreSQL storage, and a ReactJS dashboard visualized real-time and historical data. Monitoring will be conducted from late May to mid-June 2025. Findings - The system consistently captured environmental and nutrient data in real time. Nutrient concentration ranged from approximately 400 to 1,100 ppm, temperature from 27–29 °C, humidity from 65–80%, and light intensity from 180–4,780 lx. The data showed consistent temporal patterns and confirmed the system’s capability of monitoring dynamic hydroponic conditions. Research Implications - The system remains limited to monitoring without automated control, pH measurement, sensor drift evaluation, or non-Wi-Fi deployment. Originality – This study contributes an integrated end-to-end IoT architecture that combines real-time sensing, structured data management, and web-based analytics for indoor DFT hydroponics.]]>
