Optimizing Plant Growth in Indoor NFT Hydroponic Systems: Design, Environmental Monitoring, and Analysis

Hydroponics is a method of growing plants without soil, using nutrient-rich water solutions instead. However, current hydroponic systems face inadequacies in real-time monitoring precision and data synchronization across sensors. These challenges hinder the timely detection of fluctuations in key environmental factors such as pH, EC, TDS, light intensity, humidity, and temperature, which are critical for optimal plant growth. This research aims to design, implement, and analyze the effectiveness of a Nutrient Film Technique (NFT) hydroponic system for optimal plant growth. The study focuses on two primary objectives: developing an NFT hydroponic monitoring system for real-time tracking of environmental factors (pH, EC, TDS, light intensity, humidity, and temperature) and collecting comprehensive data on plant growth and development, including critical measurements like plant stem diameter and plant height. The methodology includes an in-depth literature review, hardware development with precise sensor calibration, programming for real-time data acquisition, integration of components, and system implementation. Data collection for the Dwarf Pak Choi plant species involves continuous monitoring and recording at five-minute intervals, with daily averages considered for comprehensive analysis. A dendrometer is integrated to enhance precision in stem diameter measurement. Calibration results indicate the system’s accuracy, with measured values closely aligning with theoretical expectations. Data analysis from day 40 to day 48 showcases the system’s ability to maintain optimal conditions. Electrical conductivity (EC), pH, dissolved oxygen (DO), and total dissolved solids (TDS) values, along with lux levels, consistently meet the desired range for Dwarf Pak Choi growth. In conclusion, the NFT hydroponic system successfully achieves its objectives, providing precise real-time monitoring and comprehensive plant growth data.