Designing an Automated Indoor Nutrient Film Technique (NFT) Hydroponic System for Microgreens: Integrating Internet of things (IoT)- Based Artificial Intelligence (AI) Control with Algae Prevention and Detection ReinzIel Earl C. Astrera, Edgardo Jr. R. Belay, Jonathan A. Calosa, Joven C. De Jesus, Bethoven Q. Oliveros and Rogelio E. Salalim..-

Bachelor's Thesis

College of Engineering..- Bachelor of Science in Electrical Engineering: Technological University of the Philippines, 2025.

Includes bibliographic references and index.

This study presents an automated indoor Nutrient Film Technique (NFT)
hydroponic system for microgreen cultivation, incorporating IoT and AI to overcome
traditional farming limitations like scarce arable land, pest infestations, and ineffective
algae detection. The system enables real-time monitoring and automated control of
critical parameters, including pH, electrical conductivity (EC), oxidation-reduction
potential (ORP), temperature, and humidity, to optimize growth conditions. A web-based
application allows remote access for farmers to view data and make adjustments,
streamlining operations. An integrated ozone generator suppresses algae growth,

preserving nutrient solution integrity and promoting healthy plant development. AI-
driven deep learning processes images and sensor data to identify early algae

proliferation and plant diseases, facilitating timely interventions. Radish microgreens
were tested due to their quick germination and high yields, revealing superior health and
uniformity compared to soil methods, which suffer from uneven light, nutrient rivalry,
and pests. As a scalable model, it addresses food security needs, with future upgrades
including additional sensors, AI enhancements, and improved enclosures for better
control.

Keywords:
Nutrient Film Technique (NFT); Internet of Things (IoT); Artificial Intelligence
(AI); Real time Monitoring; EC (Electrical Conductivity); ORP (Oxidation reduce
potential); Microgreens (Radish); pH;